>>WELCOME TO DATAWORKS
YOU'LL HEAR FROM THE LAB
REMOVING METADATA BARRIERS AND
TEAM CAPTAIN.
AND I WANT TO CONGRATULATE ALL
OF OUR WINNERS.
THIS IS A PERFECT TIME TO
HIGHLIGHT THE IMPACT OF
INNOVATIVE DATA SHARING AND
PRACTICES.
I'M SURE MOST OF YOU KNOW THAT
THE 2023 POLICY JUST WENT INTO
EFFECT OVER THREE MONTHS AGO
TODAY SO WE'RE CELEBRATING THAT
AND ALSO RECOGNIZING AND
REQUIRING THAT NIH RESEARCHERS
CREATE AND PLAN FOR DATA STORAGE
AND DATA SHARING.
I HOPE THESE STORIES INSPIRE YOU
WHEN YOU'RE THINKING ABOUT YOUR
DATA MANAGEMENT AND SHARING
CAPABILITIES.
THROUGH THIS AND OTHER POLICIES
NIH IS SUPPORTING A CULTURE OF
DATA SHARING AND DATA REUSE.
YOU'LL ALSO OVER HEAR DATA
SHARING AND REUSE THAT IS
SUPPORTING IN ADVANCEMENTS IN
HUMAN HEALTH.
I ENCOURAGE YOU TO JOIN ME IN
CELEBRATING OUR 2022 WINNERS AND
EXAMINE HOW YOU AND YOUR
COLLEAGUES CAN USE SOME OF THESE
APPROACHES IN YOUR OWN RESEARCH.
WE'LL USE sli.do FOR Q&A.
IT'S NUMBER 1379.
I'M EXCITED AND HOPEFUL THAT WE
CAN CONTINUE THIS EXCELLENT
PARTICIPATION AND COLLABORATION
WITH FASEB IN 2023 AND BEYOND.
SO LET'S GET STARTED.
JOIN ME IN WELCOMING DR. JAKOB
SEIDLITZ AND ANDREIA VACONCELLOS
FARIA TO TELL US ABOUT THEIR
WORK AND THE BRAIN CHART FOR
HUMAN LIFE-SPAN AND THANKS AGAIN
AND WELCOME.
OVER TO YOU JAKOB.
>>AWESOME.
THANK YOU SO MUCH SUSAN FOR THE
INTRO AND FOR THE OPPORTUNITY TO
SPEAK WITH YOU ALL ABOUT OUR
WORK.
I'M JUST GOING TO SHARE MY
SCREEN.
HERE WE GO.
AND I'LL TALK BRIEFLY ABOUT OUR
PROJECT CALLED BRAIN CHARTS FOR
THE HUMAN LIFE-SPAN.
GENERALLY THIS IS A GRASSROOTS
EFFORTS.
ME AND A FEW OTHERS STARTED THIS
AT THE BEGINNING OF THE PANDEMIC
AND OUR RESPECTIVE HOME OFFICES
AND IT QUICKLY GREW TO OVER 200
CO-AUTHORS AND THE RESULTING
PUBLICATION.
I WANT TO CENTER THE DISCUSSION
TODAY IN TERMS OF OUR WORK AND
THE WORK IN GENERAL AROUND THIS
IDEA OF DATA REPURPOSING.
WHICH I THINK HIGHLIGHTS THE
LINK BETWEEN DATA SHARING
PRACTICES AND DATA REUSE
PRACTICES TOGETHER ALONGSIDE OF
EXPERTISE SCIENTIFIC QUESTIONS
AND INNOVATION FACILITATE
SCIENTIFIC DISCOVERY.
I THINK ULTIMATELY OUR EFFORTS
CENTERED AROUND TWO VERY
FUNDAMENTAL QUESTIONS THAT HAVE
ELUDED US FOR DECADES IN HUMAN
NEUROSCIENCE.
THE FIRST OF WHICH IS THIS VERY
BASIC QUESTION OF BRAIN SIZE.
IF YOU ASK GOOGLE OR CHAT VPT
HOW BIG THE BRAIN IS IT GIVES
YOU REASONABLE AVERAGES.
THEY WILL ASK ABOUT WHEN.
ABOUT WHO.
ABOUT DIFFERENT CLINICAL
CONDITIONS ETC.
THE POINT BEING IS THAT WE HAVE
YET TO REALLY MAP THIS SIMPLE
PARAMETER ABOUT BRAIN STRUCTURE
ACROSS THE LIFE SCORES AND I'LL
GIVE SOME INSIGHT.
THE OTHER REALLY FUNDAMENTAL
ASPECT OF CLINICAL BRAIN SCIENCE
IS THAT WE DON'T REALLY MEASURE
BRAIN HEALTH.
AND THIS PUTS FORTH THE NATURAL
QUESTION IS HOW ARE WE SUPPOSED
TO MEASURE BRAIN DISEASE.
WE MEASURE IN UTERO WITH SIMPLE
MEASUREMENTS AND POSTNATALLY WE
USE GROWTH CHARTS.
WHICH HAVE BEEN AROUND FOR OVER
A COUPLE OF HUNDRED YEARS.
THIS IS ONE OF THE FIRST PLOTS.
OF HEIGHT.
AND YOU FAST-FORWARD TODAY AND
THE PEDIATRICIAN'S OFFICE -- THE
ONLY WAY IS WITH A TAPE
MEASURERRER.
SO MRI IS A POPULAR SENSE.
SO EVEN IF YOU WERE TO STUDY
SOMEONE YOU WOULD STILL ONLY GET
ABOUT 30 YEARS.
YET WE CAN USE MRI TO MAP PEOPLE
ACROSS ANY AGE FROM PREGNANCY
ALL THE WAY UNTIL LATE
ADULTHOOD.
AND THIS PLOT IS REALLY
STAGGERING IN TERMS OF THE SCOPE
OF MRI TECHNOLOGY RELATIVE TO
THE CLINICAL INFRASTRUCTURE AND
THE ULTRASOUND ASPECT IN UTERO.
AND ONE OF THE THINGS FOR THE
TRAINEES IS WHEN YOU LOOK AT THE
LITERATURE SOMETIMES YOU SEE
PLOTS LIKE THIS FROM THE
WONDERFUL REVIEW PAPER FROM JOHN
GILMORE YOU HAVE THE ESTIMATES
OF HOW THE BRAIN CHANGES OR
PROPERTY CHANGES ACROSS TIME AND
PEOPLE.
BUT THIS IS QUALITATIVELY
STITCHING TOGETHER PREVIOUS
LITERATURE AND WE LACK STANDARDS
FOR MEASURING BRAIN SIZE OR
BRAIN GROWTH ACROSS THE
PARI-NATAL PERIOD AND THIS
NOTION OF THIS STUDY IN OUR
FIELD ENCOMPASSES 90% OF
LIFE-SPAN STUDIES START ABOUT
2-5 YEARS OF AGE AND THIS
PROBLEM OF BIRTH WHERE WE CAN
MEASURE MRI IN UTERO BUT
TYPICALLY THOSE STUDIES ARE NOT
TRAVERSING THAT BIRTH WITHIN AN
INDIVIDUAL PERSON SO WE'RE NOT
LEARNING ABOUT AN INDIVIDUAL'S
TRAJECTORY.
SO I JUST WANT TO HIGHLIGHT THAT
THERE ARE MORE THAN A FEW
PORTABLE MRI TECHNOLOGIES THAT
HAVE COME ON-LINE.
SO THE SCALE OF MRI AND THE
SCOPE IS DRASTICALLY CHANGING.
AND THE OTHER OPPORTUNITY IN
THIS SPACE IS THAT WE CAN
QUANTIFY BRAIN DEVELOPMENT OR
BRAIN GROWTH REALLY ROBUSTLY
WITH AMAZING OPEN SCIENCE TOOLS
AND RESEARCH GRADE TOOLS AND
PREVIOUS SCIENTIFIC DISCOVERIES
BEFORE AND THIS IS A VIDEO FROM
A COUPLE OF OUR CO-AUTHORS AND
YOU CAN SEE THE EXPANSION AND
THE FOLDING THAT OCCURS
ESPECIALLY LATER DURING
PREGNANCY.
AND PRIOR TO A FEW YEARS AGO --
USED SOME OF THESE BRAIN CHARTS
NOT OURS BUT SOME BRAIN CHARTS
FROM A SMALL DATASET BETWEEN 0-5
TO MAP THE RESPONSE FOR A SHUNT
FOR HIDE THROW AT THE PLUS *
PATIENTS.
YOU CAN SEE THAT USING THESE
CHARTS THE SUBJECT CAN BE
MATCHED FROM HAVING A LOW BRAIN
SIZE TO BEING KIND OF BACK ON
TRACK AFTER INTERVENTION.
SO THERE IS A CLEAR CLINICAL
NEED FOR THIS AND MANY PEOPLE
HAVE MAPPED VARIOUS CASE VERSUS
CONTROL EFFECTS.
SO THIS TYPE OF FRAMEWORK IS
USEFUL TO MAP BRAIN DEVELOPMENT
AND AGING SO WE CAN BETTER
UNDERSTAND DISEASE AND COMPARE
ACROSS DIFFERENT CLINICAL
CONDITIONS AND THE PRECEDENT
HERE IS STAGGERING WITH ALL OF
THE AVAILABLE RESEARCH DATASETS
AND SOME OF WHICH ARE LISTED
ABOVE AND CLINICALLY SCANNING IN
THESE VULNERABLE POPULATIONS IN
KIDS UNDER FIVE ON THE RIGHT AND
ADULTS OVER 65 IS STEADILY
INCREASING.
ABOUT 3% YEAR ON YEAR GROWTH.
THAT IS OVER 10 MILLION MRI
SCANS JUST BEING DONE IN THE
U.S. AND THAT IS FROM 2019-2020.
SO THIS DATA EVEN IF YOU WANT TO
FIND THEM AND WITH AMAZING TOOLS
THAT EXIST IN OUR COMMUNITY FOR
HOW TO ORGANIZE DATA AND SHARE
DATA STILL YOU HAVE TO LOOK IN
MANY APPLICATIONS.
BUT THIS IS HIGHLIGHTING A FEW
RESOURCES BUT WE OURSELVES
ACCESS SOME DATA AND SOME OF THE
PROTOCOLS TO HELP FACILITATE
DATA SHARING AND DATA ACCESS.
SO OUR PROJECT BRIEFLY A FEW
DIFFERENT GOALS.
ONE WAS ON THE SCIENTIFIC SIDE.
TO MAP POPULATION LEVEL,
TRAJECTORY TO DEFINE MILESTONES
OF BRAIN GROWTH AND MAKE THIS
TOOL AVAILABLE FOR PEOPLE WITH
DATASETS OF THEIR OWN THAT WERE
NOT INCLUDED IN THE ORIGINAL
STUDY AND BECAUSE A LOT OF
DATASETS ARE CASE CONTROL IN
DESIGN WE WANTED TO PROVIDE
STANDARDIZED EFFECT SIZES FOR
PATIENT POPULATIONS WITH
SUFFICIENT SAMPLE SIZES AND WE
WANTED TO MAKE THE DATA ACCESS
FOR OTHER RESEARCHERS MORE
ACCESSIBLE.
AND THIS IS THE BASIC IDEA.
TAKE THE BRAIN SCAN AND DISSECT
IT AND WE CAN DO THAT ACROSS THE
ENTIRE POPULATION OF DATA THAT
WE HAVE AND USE SOME GROWTH
MODELING TO BE ABLE TO PROVIDE
THESE DIFFERENT CURVES FOR SOME
TILES AS YOU WOULD FOR BREATHE
POPULATION CHARTS.
OUR SAMPLE WAS ABOUT 125,000
SCANS.
WE ACCESS DATA FROM ACROSS THE
ENTIRE MRI LIFE-SPAN SOEVER
SINCE MRI CAME ON-LINE AT SCALE
IN THE EARLY '90s UP TO A FEW
YEARS AGO MEANING 115 DAYS POST
CONCEPTION DIFFERENT SITES AND
SCANNERS AND MASSIVELY GLOBAL.
NOT FULLY POPULATION
REPRESENTATIVE AND ACROSS
DIFFERENT CLINICAL CONDITIONS.
THESE DATASETS WERE RANGING IN
TERMS OF SHAREABILITY AND
OBVIOUSLY REQUIRED A TON OF
COMPUTE TIME.
WE TAKE A SCAN AND BREAK IT DOWN
INTO DIFFERENT PIECES AND USE A
STATISTICAL FRAMEWORK TO ASK THE
QUESTION OF HOW BIG IS THIS
BRAIN REGION FOR EXAMPLE
RELATIVE TO OTHER PEERS OF THAT
INDIVIDUAL AND WE CAN LOOK FOR
DETAILS AS BEST AS POSSIBLE.
AND THESE ARE THE RAW VALUES OF
BRAIN STRUCTURE AND THE RAW DATA
IS ON TOP.
THAND THE NORMATIVE VARIANTS.
THE DIFFERENCE ACROSS THE
POPULATION IN MAGNITUDE ACROSS
THESE DIFFERENT FEATURES ARE
MAPPED ACROSS THE LIFE-SPAN.
AND WE CAN MAP THE PACE OR THE
VELOCITY.
WE CAN DO THIS FOR GLOBAL
FEATURES AND ALSO FOR DIFFERENT
LOCAL FEATURES, BRAIN VOLUMES
FOR REGIONS IN THE CORTEX AND
THE SUB CORTEZ AND THIS IS
HIGHLIGHTING THE RANGE AND
DEVELOPMENT FOR THE TWO EXTREME
FEATURES IN TERMS OF WHEN THEY
PEAK IN OVER-ALL SIZE.
HERE MAPPING THE PERCENTAGE OF
TOTAL MAXIMUM SIZE FOR EACH OF
THE DIFFERENT FEATURES SO WE CAN
USE THESE TYPES OF PLOTS TO MAP
DIFFERENT PHASES OF DEVELOPMENT.
TIMES WHEN THESE FEATURES ARE
CHANGING MOST VERSUS CHANGING
LEAST.
WHEN THEY ARE COLLECTIVELY
CHANGING VERSUS DIVERGING.
TO BE ABLE TO BETTER UNDERSTAND
AND MAP OUT IDEAL MILESTONES OR
IDEAL PERIODS TO QUERY
INDIVIDUALS ESPECIALLY FOR
LONGITUDINAL DESIGN.
AND OUR VISION FOR THIS WORK IS
MULTI-FOLD.
THIS IS MY FELLOW FIRST
CO-AUTHOR RICHARD BETHLEHEM
QUERYING OUR ON-LINE TOOL THAT
IS AVAILABLE TO ANYONE WHERE IT
REQUIRES A BASIC SPREADSHEET
WITH PROCESS DATA AND YOU CAN
CONVERT THESE QUANTITIES THESE
BRAIN METRICS THAT ARE DRIVABLE
FROM MULTIPLE PIPELINES AND
CONVERT THEM TO THESE SCORES.
YOU CAN VISUALIZE YOUR OUTPUT.
DOWNLOAD RESULTS.
YOU CAN EXPLORE THE ENTIRE
DATASET.
AND WE HOPE THIS IS JUST A START
TO PROVIDE MORE OF A SUSTAINABLE
DEVELOPMENT IN THE FUTURE.
AND AS I SAID SOME OF THE POWER
OF THE IMAGING IS UNLOCKED ONCE
YOU'VE NORMALIZED YOUR DATA AND
PROVIDED BENCHMARKS RELATIVE TO
A MUCH LARGER DATASET.
YOU CAN LEVERAGE STUDIES THAT
ARE MORE DEEPLY PHENOTYPED
WITHIN INDIVIDUALS AND YOU CAN
USE MRI AS ROBUST END POINTS
WHETHER FOR PREDICTION IF YOU'RE
LOOKING EARLY OR PRETREATMENT OR
USING THEM AS END POINTS TO LOOK
AT OUTCOMES.
RESULTS OF AN IMAGING GENETIC
STUDY OR COMPARING TO ASPECT OF
BEHAVIOR AND COGNITION ETC.
SO WITH THAT THE MAIN LESSONS
THAT WE REALLY LEARNED THIS IS
THREE OF MANY THE FIRST TWO ARE
LINKED TO EACH OTHER IN THAT
DATA SHARING AND REUSE ARE
RESEARCH INTENSIVE.
INHERENTLY COLLABORATIVE
ENDEAVORS.
IT ENDS UP AS A PUBLICATION OR
PRODUCT AND THERE ARE SOME
HIGHLIGHTED AN CITY LARRY
PUBLICATIONS TO OURSELVES.
*.
AND DATA REPURPOSING IN A GLOBAL
SENSE REQUIRES PEOPLE WITH
VARYING EXPERTISE AND THAT IS
INTIMIDATING TO EARLY STAGE
RESEARCHERS BUT MOST OF THESE
THINGS CAN BE INITIATED WITH
GOOD MENTORSHIP FROM PEOPLE IN
THE TRAINING PROCESS.
I WAS A POSTDOC AT THE TIME OF
STARTING THIS PROJECT FOR
EXAMPLE.
AND MORE GENERALLY WE DEFINITELY
RAN INTO THIS PROBLEM OF THE
POPULATION REFERENCE AND THE
LACK OF EQUITY AND IT COMES WITH
THE FIRST TWO POINTS.
WE HAD ACCESS TO FREE COMPUTES
AT OUR UNIVERSITIES AND DATA
STORAGE, LEGAL ACCESS FOR DATA
SHARING AND SO OPEN SCIENCE OUR
EFFORTS ARE AMAZING AT
INCREASING EQUITY IN TERMS OF
THE DEMOCRITAZTION OF ACCESS AND
RESOURCES BUT THEY ARE JUST THE
FIRST STEP IN A LARGER
SCIENTIFIC PROBLEM FOR
INCREASING THE GLOBAL
REPRESENTATIVENESS OF THE
DATASETS THAT ARE BEING SHARED
AND SO WITH THAT I WOULD LIKE TO
THANK THE CO-AUTHORS OF THE
PAPER AND FOR ALL OF YOU FOR
YOUR ATTENTION.
>>THANK YOU SO MUCH, JAKOB.
OUR NEXT SPEAKER IS FROM THE
JOHN HOPKINS UNIVERSITY AND
YOU'RE ALSO GOING TO SPEAK ABOUT
USING PUBLIC DATA TO GENERATE
NEW DISCOVERIES SO OVER TO YOU
DR. FARIA.
>>HI.
LET ME SHARE MY SCREEN.
CAN YOU SEE MY SCREEN?
>>YES.
LOOKS GREAT.
THANK YOU SO MUCH.
>>THANK YOU FOR THE INVITATION
TO TALK TODAY AND I'M GOING TO
TALK ABOUT THE FAIR ANNOTATED
DATA OF STROKE MRI'S AND
METADATA.
SO AS YOU KNOW STROKE IS HUGELY
IMPORTANT FROM BOTH RESEARCH AND
CLINICAL POINTS.
WE CAN USE THIS QUANTITATIVE AND
OBJECTIVE INFORMATION TO GUIDE
CLINICAL TRIALS OR DECISIONS
ABOUT TREATMENT OR EVEN TO
CREATE TOOLS TO BE SECOND
FEEDERS.
PATIENT EDUCATION ETC.
WE HAVE RESEARCH IN THESE
STUDIES THAT LEAD TO MOST OF THE
KNOWLEDGE IN NEURO SIGH THANS WE
HAVE TODAY AND WE MOVED FROM THE
ADOPTIVE EXPERIENCES TO DATA
SUPPORTED EVIDENCE.
AND THAT IS THE SOLUTION AND THE
PROBLEM.
SO WE NEED LARGE AMOUNT OF DATA
TO STUDY STROKES AS WELL AS --
DATA IMAGING AND METADATA.
IT HAS -- IT IMPOSE NEW
CHALLENGE TO DATA SHARING.
BUT WE ARE ABOUT TO OVERCOME
SOME OF THIS CHALLENGE SHARING
THE FIRST LARGE DATASET OF
CLINICAL STROKES CLINICAL
ACCUSED AND STROKES AND THE
DESIGN IS THAT WE TEAM UP WITH
OUR RESEARCH CENTER SO WHENEVER
PATIENTS WERE SCANNED WITH
STROKE SYMPTOMS WE TRANSFER
THEIR IMAGE DATA AND ANNOTATE IT
AND THEN WE GOT THE STRUCTURAL
INFORMATION, THE METADATA
INFORMATION ABOUT DEMOGRAPHICS
ABOUT CLINICAL FILE.
AND WE ORGANIZE IT ALL FOR BITS
AND GUIDELINES AND WE MAKE IT
PUBLICLY AVAILABLE AS I'M GOING
TO SHOW YOU NEXT.
AND WHAT IS THE IMPORTANCE OF
THIS?
THE IMPORTANCE IS THAT WE CAN
GENERATE FEATURE FACTORS OR
COMPUTABLE OBJECTS.
REVEALING HOW DIFFERENT LESIONS
AFFECT DIFFERENT REGIONS OF THE
BRAIN AND THIS IS POWERFUL FOR
RESEARCHERS IN DIFFERENT FIELDS.
FOR INSTANCE CLINICIANS OR
CLINICAL RESEARCHERS CAN
DIRECTLY USE THIS DATA IN THEIR
LESION BASED STUDIES OR TO GUIDE
CLINICAL DECISIONS LONG-TERM.
AND BIOMEDICAL ENGINEER AND
TRANSLATION RESEARCHERS CAN USE
THIS DATA TO DEVELOP FOR
INSTANCE NEW TOOLS FOR STROKE
ANALYSIS AND QUANTIFICATION.
HERE IS WHERE WE ARE NOW.
SO WE SHARED THESE ALMOST 3,000
PATIENTS WITH THE IMAGE AND THE
METADATA OF THESE PATIENTS.
THERE ARE LESIONS IN DIFFERENT
REGIONS OF THE BRAIN.
DIFFERENT TYPES OF STROKES AND
WE SHARE NOT ONLY THE RAW DATA
BUT ALSO THE IMAGE ANNOTATED,
THE STROKE MASK AND THE BRAIN
MASK AND THE IMAGE IN COMMON --
COORDINATES AND IMAGE ANALYSIS.
SO WE CAN EXPENSE DATA USAGE AND
STRUCTURE THE METADATA
DEMOGRAPHIC AND CLINICAL
INFORMATION.
WE SHARE THIS IN THE
PROFESSIONAL REPOSITORY.
WE USE THE ICPSR.
ONE OF THE GOOD POINTS ABOUT
SHARING THESE IN THE THIRD PART
REPOSITORY IS THAT IT ALLEVIATES
THE BURDEN THAT SOMETIMES THE
RESEARCHERS UNDERSTAND ESTIMATE
THE BURDEN OF DATA CREATION AND
DATA MAINTENANCE AND ALSO IT
GIVES THE PROFILES ABOUT DATA
USAGE THAT ARE VERY IMPORTANT TO
TRACK YOUR DATA SHARING.
AND HERE I WOULD LIKE TO SHOW
YOU SOME OF THE APPLICATIONS
THAT WE AND OTHERS ARE USING
THIS DATA FOR.
SO HERE IS THE ARTERIAL
TERRITORIES BRAIN ATLAS WITH
MORE THAN 1300 LESION MARKS.
THIS IS A DIGITAL ATLAS.
THE FIRST EVER AVAILABLE.
WE ALSO FREELY SHARE THESE TOOLS
IN METRIC AND AS YOU CAN SEE WE
ARE RECEIVED BY THE COMMUNITY
BECAUSE NOW PEOPLE ANYONE CAN GO
THERE, DOWNLOAD AND CHECK LESION
LOADS AND OTHER FEATURES IN EACH
BRAIN.
WE DEVELOP ACCESSES BASED ON
STROKE ANALYSIS AND NOW THAT WE
KNOW WHERE DIFFERENT STRUCTURES
OF THE BRAIN ARE WE ALSO NEEDED
TO KNOW WHERE THE STROKE WAS IN
A QUANTITATIVE AUTOMATIC WAY.
SO USING THE DATASET WE CREATED
A MODEL FOR STROKE DETECTION AND
SEGMENTATION THAT UTILIZE
TRADITIONAL MACHINE LEARNING AND
AS YOU CAN SEE WE WERE ABLE TO
CREATE MODELS OR CREATE THE
AGENTS THAT ARE PRETTY CLOSE TO
MONO DELINEATION.
THIS IS SAYING WHERE THE STROKE
IS AND WHERE DIFFERENT VISIONS
OF THE BRAIN WERE SO WE CAN
TRANSFORM THIS DATA INTO NUMBERS
WHICH I'M CALLING COMPUTABLE
DATA OBJECTS AND WITH THESE
NUMBERS WE COULD USE IT IN A
MUCH MORE FRIENDLY WAY.
TO CALCULATE INCHES OF CLINICAL
RELEVANCE OR TO DO REPORTS OR TO
DO LESION BASE STUDIES.
HERE ARE SOME OF THE EXAMPLES.
SO ONE OF OUR FIRST APPLICATIONS
WAS TO USE THIS QUANTITATIVE
FEATURE FACTOR SAYING WHICH
REGIONS ARE ORIGINATED BY THIS
STROKE.
TO CREATE ATLAS SCORES.
PEOPLE THAT ARE NOT FAMILIAR
WITH STROKE, USE IT IN CLINICAL
ANALYSIS THAT KEEPS YOU WHICH
REGIONS ARE AFFECTED BY THE
STROKE AND THEN WE CAN MODEL
YOUR PROGNOSIS OR THINK ABOUT
ACTUAL TREATMENT.
SO WE USE IT -- THIS DATA TO
TRAIN MACHINE LEARNING MODELS
AND CALCULATE SCORES AND WE NOT
ONLY DELIVER THIS COURSE BUT
ALSO THE COMPREHENSIVE FEATURES
THAT WERE USED ON THESE MODELS
TO ARRIVE ON THESE OUTPUTS.
SO THESE ARE COMPREHENSIVE
MODELS WHICH ARE VERY WELL
RECEIVED IN THE CLINICAL
COMMUNITY.
AND FOLLOWING THIS SAME
REASONING WE ALSO CALCULATED
FEATURE FACTORS BASED ON WHOLE
BRAIN SEGMENTATION AND WE WERE
ABLE TO PRODUCE REPORTS
AUTOMATED REGIONAL REPORTS THAT
AGREE VASTLY WITH REGIONAL
REPORTS.
THIS IS IN AGREEMENT WITH WHAT I
THINK HAS TO BE ONE OF THE MAIN
GOALS IN ARTIFICIAL INTELLIGENCE
APPLICATIONS IN HEALTH
CONDITIONS.
THIS CANNOT BE SUPER USEFUL IN A
VERY BIG CENTER BUT IT CAN ACT
AS A SECOND FEEDER OR A FLAG IN
THE CENTER.
IT ALSO PRODUCED NUMBERS
QUANTITATIVE NUMBERS.
THAT SAVE US THE BURDEN AND THE
TIME TO GO THERE AND TO USE THIS
TO DO YOUR CLINICAL PLANNING OR
YOUR RESEARCH.
.
AND ALSO THIS IS FREE AND
PUBLIC.
ANYONE CAN GO THERE AND DOWNLOAD
THIS TECHNOLOGY.
AND CAN IT'S VERY USER-FRIENDLY
SO YOU CAN RUN THIS PROCESS IN
YOUR CPU WITH ONE COMMON LINE
AND YOU CAN GET THE REPORTS OF
QUANTITATIVE FEATURE FACTORS AND
ALSO IF YOU WANT OTHER TYPES OF
MEDICAL AND CLINICAL RESEARCH
REPORTS.
I'M GOING TO GO QUICKLY ABOUT
OUR INITIAL APPLICATIONS.
AND THE MEASURES OR DO THE MORE
TRADITIONAL LESION BASED STUDIES
CORRELATING THEM WITH BRAIN
FUNCTION.
AND I THINK AND I WANT AND WE
ARE WORKING TO -- TOWARDS IS TO
CREATE THIS ECOSYSTEMS AND
CREATE FRIENDLY TOOLS TO EXTRACT
COMPUTABLE DATA SO PEOPLE CANNOT
ONLY USE THIS REPOSITORY BUT
THEY CAN CONTRIBUTE AND SHARE
DATA AND YOU CAN EXPAND THE
NUMBERS AVAILABLE FOR TESTING
SETS OR FOR TRAINING SETS AND
DOING THAT TYPE FOR EVERYBODY.
THANK YOU FOR THE OPPORTUNITY TO
TALK TO YOU TODAY AND SHOW YOU
WHAT WE'VE BEEN DOING.
>>THANK YOU SO MUCH.
AND I WOULD LIKE TO ASK OTHER
PANELISTS TO JOIN IN THE
CONVERSATION AS WE HAVE A FEW
QUESTIONS COMING IN FROM
sli.do.
>>WE RETAIN AND WE OFFER THIS
INFORMATION -- WE OFFER ALL OF
THE INFORMATION THAT WE WERE
ALLOWED TO SO AGE FOR INSTANCE
WE DON'T OFFER INFORMATION ABOUT
PATIENTS OR 90 YEARS OLD OR
BELOW TO YEARS OLD AND THAT IS
THERE AVAILABLE FOR ANYONE TO
USE.
FROM THESE MODELS WE MADE
INITIALLY SO, IT'S NOT -- THE
INITIAL APPLICATIONS.
THE ONES TOWARD STROKE
SEGMENTATION AND THE STROKE
CALCULATION OF DIFFERENT SCORES,
NO, WE DIDN'T USE ANY OTHER
INFORMATION OTHER THAN IMAGE.
BUT THERE ARE OTHER APPLICATIONS
WHERE THIS INFORMATION WOULD BE
VERY VALUABLE.
AND THAT IS WHY WE MADE IT
AVAILABLE.
>>THANK YOU.
THAT IS WONDERFUL.
WE HAVE A QUESTION THAT IS FOR
BOTH PANELISTS.
DO YOU ENVISION PROVIDING
STANDARDIZING TEMPLATES THAT
WOULD SUPPORT USERS TO ANALYZE
AND UPLOAD AND SHARE ADDITIONAL
DATA.
THANK YOU FOR THIS.
JAKOB.
DO YOU MIND ANSWERING FIRST.
>>REALLY GOOD QUESTION.
GENERALLY WE HAVE A COUPLE BASIC
TEMPLATES FOR THE CURRENT STATE
OF HOW OUR TOOLS ARE OPERATING
WHICH IS FAR DOWN THE BRAIN
IMAGING PROCESS.
AND ULTIMATELY WE WANT TO BUILD
A MORE END TO END COMPONENT TO
THE PIPELINE SO WITH THAT WE'LL
ALLOW USERS TO INPUT SOMETHING
THAT BASICALLY COMES OFF THE
SCANNER AND THEN YOU'LL GET THE
SAME OUTPUT.
BRINGING THE INTERFACE CLOSER TO
THE DATA WILL HELP FACILITATE
THAT MORE.
AND THEN GENERALLY IN BRAIN
IMAGING THERE IS SOME STANDARDS
THAT EXIST -- IN OUR CASE IN A
DASHBOARD OR EXPLORING DIFFERENT
ASPECTS OF BRAIN STRUCTURE OR
LESION CAPACITIES IT'S AN OPEN
QUESTION.
>>YOUR THOUGHTS, ANDREIA?
>>I AGREE.
ONE OF THE SURPRISED WHEN I
STARTED WORKING IS THAT METADATA
WAS ACTUALLY MORE CHALLENGE THAN
ORGANIZING THE METADATA WAS MORE
CHALLENGING.
IT SEEMS THERE IS MORE THE
STRUCTURES IS MORE MATURE.
SO YOU HAVE THESE PRINCIPLES ON
HOW TO SHARE.
PRINCIPLE FOR STRUCTURE LIKE
THESE.
AND I THINK METADATA HAS TO GO
THROUGH THE SAME -- ALONG THE
SAME LINE.
I BELIEVE IN THE NEXT FEW YEARS
IT-- IT'S EASIER TO ALLOW PEOPLE
PARTICULARLY IF YOU'RE ASKING
FOR INPUT TO MAKE CONSENSUS FOR
STRUCTURE OF METADATA.
>>THANK YOU.
I THOUGHT THAT WAS REALLY
INSIGHTFUL AND PROVIDING AND
STANDARDIZING ME DEBT METADATA S
CHALLENGING.
WE HAVE SPECIFIC QUESTIONS.
IN HIGH RISK PATIENTS WERE YOU
ABLE TO GET AN MRI EVERY FIVE
YEARS AND CAN WE PREDICT THE
CHANCE OF STROKE BY APPLYING
MACHINE LEARNING TO THESE DATA.
OR OTHER DATA OF BRAIN MRI'S.
I THINK I'M READING THIS
CORRECTLY.
WHAT DO YOU THINK.
HIGH RISK PATIENTS EVERY FIVE
YEARS.
>>I HAVE THE NUMBERS.
YOU CAN PREDICT -- THE
BOTTLENECK IS NOT MATHEMATICAL.
IT IS WELL OH COORDINA -- DECOOD
DATA. *.
BUT NOW REALLY ACCUMULATE WELL
COORDINATED DATA IS THE BEST WAY
TO MODEL WHAT YOU WANT TO MODEL
AND IF YOU HAVE LARGE AMOUNTS OF
DATA COLLECTED IN WHATEVER TIME
YOU CAN BE EITHER CONSISTENT OR
HARMONIZE IT.
THE LIKELIHOOD OF PREDICTIONS
EVEN IF IT IS A RELATIVELY
OUTCOME IT'S LIKELY THAT YOU'LL
GET SOMETHING THAT COMPARES WITH
REALITY.
BUT YEAH.
IT DEPENDS ON CREATING ROBUST
MODELS.
AND I GUESS NUMBERS, YOU CANNOT
BEAT REAL DATA YET.
>>THANK YOU.
LAST QUESTION THAT I SEE IN THE
SLIDO IS VERY SPECIFIC.
NO.
WE HAVE MORE.
HOW MANY ENCOUNTERS DO YOU HAVE
PER PATIENT AND IS YOUR MODEL
BASED ON TIME SERIES?
>>NO.
THIS IS CROSS SECTIONAL.
ONE PATIENT ONE IMAGE.
SO 3,000 DIFFERENT PATIENTS
BECAUSE I'M WORKING WITH ACUTE
STROKE.
SO EVERYTHING HERE IS RELATED TO
THE ACCURACY.
AND THE THINGS THAT I SHOW THE
LESION BASE STUDIES ARE THOSE
THAT TRY TO EXCLUDE CHRONIC
BRAIN PRPREMODULATION.
WE ARE NOT YET AT THE SAME
APPOINTMENT AS WE ARE FOR ACUTE
STROKE.
>>THANK YOU.
I'D LIKE TO ASK THIS QUESTION OF
BOTH OF YOU HERE IN SLIDO
BECAUSE IT'S SOMETHING THAT OUR
OFFICE IS VERY INTERESTED IN.
THE MEASURE OF AI READY DATA.
WOULD YOU HAVE SOME IDEAS ABOUT
STANDARD METRICS TO MEASURE WHAT
WE WOULD CONSIDER AI READY DATA
JAKOB, DO YOU HAVE THOUGHTS ON
THIS?
>>YEAH.
I THINK YOU HAVE A MULTI-LEVEL
HARMONIZATION PROBLEM THAT
BECOMES COMPLICATED.
AND I THINK THE FURTHER AWAY YOU
GET FROM THE ACTUAL RAW IMAGE
THE MORE CONVOLUTED THAT CAN
BECOME.
IN OUR EFFORT WE BASICALLY USED
STATISTICS TO HELP COMBAT THAT
HARMONIZATION PROBLEM.
BUT IT REALLY OUGHT TO BE AT THE
ACQUISITION WHERE WE'RE SPENDING
MORE RESOURCES ON THE
STANDARDIZATION FOR THE IMAGE
THAT IS RECONSTRUCTED AND THEN
WE CAN RUN OUR MODELS ON OR OUR
PROCESSING ON.
AND I THINK THAT IS BEING DONE.
AND IT IS DONE IN CLINICAL
TRIALS AND OTHER MULTI-SITE
DESIGNS ETC.
SO I THINK THERE IS HOPE IN
TERMS OF STANDARD METRICS FOR
THAT.
I THINK THE BEST STILL THE OLD
SCHOOL TRAVELING HEAD STUDY
INDIVIDUALS THAT ARE THAT HAVE
REPRESENTATION ACROSS THE
VARIABILITY ARE STILL HUGELY
GOLD STANDARD WHERE YOU CAN
ACTUALLY MEASURE THE IMPACT OF
SOME OF THESE NOISE FACTORS AND
THRESHOLD WISE I DON'T EVER A
GOOD NUMBER BUT MINIMIZING THAT
VARIANCE WILL INCREASE THE
SIGNAL FOR THE RESULTING OUTPUT.
>>THANK YOU.
THAT IS REALLY HELPFUL.
ANDREIA, DO YOU EVER SOME
THOUGHTS ON MEASURING THE AI
DATA ON THE WORK THAT YOU'RE
DOING?
>>YEAH.
WORKING WITH STROKE IS
CHALLENGING IN MANY SENSE BUT
THERE IS ONE POINT THAT MAKES IT
MUCH EASIER BECAUSE THE WHAT
WE'RE LOOKING AT THE DISEASE
EFFECT IS SO HUGE.
ANYONE CAN SEE THE LESION THERE
-- THAT EVEN WITH A LOT OF
POPULATION OF TECHNICAL
VARIATION THE EFFECT SIZE IS WAY
BIGGER THAN DESIRED.
HAVING SAYS THAT IT'S VERY
INTERESTING BECAUSE WE DO ALL OF
THESE ARTIFICIAL INTELLIGENCE
CONTROLS AND MODELS AND ETC. BUT
AT THE END WHEN WE'RE GOING TO
GO THERE AND CHECK HOW WE AGREE
WITH THE HUMANS OR HOW WE AGREE
WITHSTANDS WE GO TO THE
CLASSICAL STATISTICS IN GENERAL.
AGAIN JUST A QUESTION OF I
DON'T, I WOULD NOT COMPARE.
THAT IS NOT MY FIELD.
BUT I BELIEVE PEOPLE WILL FIGURE
OUT IN THE NEXT FEW YEARS HOW AI
CAN GET YOU -- AND MAYBE
SELF-CONTROL THE MODELS AND THE
HOURS.
>>THANK YOU.
WE HAVE A GOOD QUESTION.
LARGE LANGUAGE MODELS AND CHAT
GPT.
WHAT DO YOU THINK IS THE FUTURE
FOR THESE LARGE LANGUAGE MODELS.
A VERY PARTICULAR QUESTION FOR
EXAMPLE HIGHLIGHTING POSSIBLE
PATHOLOGICAL REGIONS.
I WOULD LOVE TO HAVE YOUR
THOUGHTS IN IDENTIFYING
PARTICULAR REGIONS.
ANDREIA, IF YOU WOULD LIKE TO GO
FIRST
>>I THINK -- FROM THE CLINICAL
POINT OF VIEW CONDITIONS THAT
NEED QUANEED QUALIFICATION.
QUANTIFICATION.
*.
AND THAT IS ANOTHER THING THAT
WE WILL BE THE NICHE FOR ALL OF
THESE MODELS.
WE ARE NOT THERE AND WE DON'T
WANT TO GO THERE.
I THINK IT'S ALL ABOUT OFFERING
HELP, HELPING ON SAVING TIME OR
HELPING FLAG SOMETHING THAT IS
-- A KNOWN EXPERIENCED PERSON,
WE TALK ABOUT DEMOCRATIZING BOTH
HEALTH CARE AND RESEARCH.
AND ALSO ABOUT GUIDING THE
EFFORT THE HUMAN EFFORT TO
SOMETHING THAT MAY BE ABOVE --
LIKE TRACING THE LARGER PLANES
FOR DATA USAGE AND DEMOCRATIZING
IN BETWEEN EXPERTISE.
IT'S VERY HARD FOR PEOPLE
WORKING IN IMAGE BECAUSE IT HAS
TO BE AN IMAGE EXPERT TO EXTRACT
BRAIN VOLUMES AS JAKOB DID BUT
CLINICIANS WANT TO USE THESE AND
THEY DON'T WANT TO DO FIVE YEARS
TRAINING.
SO I THINK THAT IS THE NICHE OF
AI TO BE ABLE TO OFFER DATA TO
PEOPLE WITH DIFFERENT EXPERTISE
CAN COMMUNICATE AND DEMOCRATIZE
THE FRUITS.
>>JAKOB ...
>>ABSOLUTELY.
ANDREIA TOUCHED ON THE POINTS.
THE SIMPLEST THINGS THAT IT WILL
EXPEDITE HYPOTHESIS GENERATION.
TO USE TOOLS.
SO ABSOLUTELY.
EXPEDITE THAT AND IT WILL
EXPEDITE THE FAIL RATE.
MOST OF WHAT WE DO IS WE BANG
OUR HEADS A AGAINST THE WALL TO
SOLVE THE PROBLEM.
BUT IT WILL BE A QUICKER FAIL
RATE.
AND THAT IS EXCITING BECAUSE
THAT CHANGES THE LANDSCAPE FROM
A SCIENTIFIC TRAINING
PERSPECTIVE OF HOW TO INTERACT
WITH THESE TOOLS AS A MENTOR
VERSUS A STUDENT ETC.
AND I FIND THOSE ISSUES
ALONGSIDE THE ETHICAL ISSUES
REALLY FASCINATING BUT FOR OUR
TOOL ULTIMATELY IT WILL BE
REALLY AMAZING TO BE ABLE TO ASK
QUESTIONS ABOUT WHERE TO LOOK IN
THE BRAIN.
FOR A GIVEN CONDITION OR PATIENT
WITH THE ELECTRONIC HEALTH
RECORDS.
IT WILL MAKE THESE THINGS MORE
EFFICIENT AND ROBUST.
THEY WILL STILL SUFFER FROM BIAS
IN CERTAIN CASES BUT ULTIMATELY
IT WILL BE VERY GOOD AIDS FOR
OUR WORK.
>>WONDERFUL.
I HAVE ANOTHER QUESTION IN THE
SLIDO.
I'M THINKING ABOUT THIS PRIZE
CHALLENGE AND YOUR
PARTICIPATION.
WHERE DO YOU SEE SIM LASH
INCENTIVES FOR PROMOTING DATA
REUSE AND YOUR OPPORTUNITY
PONTIFICATED ABOUT THIS
CHALLENGE.
I WOULD LOVE TO HEAR YOUR INPUT
ON THIS CHALLENGE.
>>JAKOB, DO YOU WANT TO TAKE
THAT ONE FIRST?
>>I'M THINKING ON THE SPOT AS
WELL ABOUT OTHER OPPORTUNITIES
THAT I'VE SEEN.
AND I MAY NOT HAVE A DIRECT
ANSWER TO THAT RIGHT NOW.
THE IRONY OF ME SITTING TODAY A
CO-AUTHOR PINGED US THIS
ANNOUNCEMENT.
ULTIMATE LEADIT -- THE PARTICULR
THESE PURPOSES THAT ARE
NONINSTITUTIONAL.
INSTITUTION WOULD BE GREAT.
BIAS FOR THE BIG MONEY
INSTITUTIONS AND DEDICATED
ASSETS FOR THAT LOCALLY BUT
HAVING THESE GLOBAL BIG WELL
ESTABLISHED FUNDERS BE THE ONE
THAT'S ARE FACILITATING THESE
AWARDS IS ULTIMATELY GOING TO
GET PEOPLE'S ATTENTION.
>>THANK YOU.
ANDREIA, WHAT ARE YOUR THOUGHTS
ON THIS CHALLENGE AND OTHER
INCENTIVES AND HOW CAN WE
PROMOTE DATA REUSE.
>>YEAH.
IT'S ONE -- IT HAS TO BE MORE --
RECOGNITION.
THERE IS NO OTHER WAY TO GO IN
DATA SCIENCE IF NOT THROUGH
COLLABORATION AND SHARING DATA.
IT SEEMS VERY TIME-CONSUMING, IT
MAY CONSUME 80%-90% OF YOUR
WHOLE RESEARCH TIME AND IT'S
INEVITABLE AND IT LEADS YOU TO
NICE DISCOVERIES.
RELIABLE DISCOVERIES.
AND I THINK GRANTS, CHALLENGE.
SCIENTISTS LIKE CHALLENGES.
ALSO OFFERING MAYBE COMMON
RESOURCES LIKE REPOSITORIES.
THIS IS VERY IMPORTANT.
YOU MAY ACCUMULATE NICE AMOUNTS
OF DATA AND THAT IS ONE
CHALLENGE WE HAVE WHICH
REPOSITORY WOULD WE USE.
THAT WOULD AFFECT LARGE AMOUNTS
OF DATA.
MAYBE FUNDING AND REPOSITORIES
AND WAYS TO SHARE DATA WOULD BE
A NICE DIRECTION.
>>WONDERFUL.
BEFORE I GET TO THE LAST
QUESTION I WANT TO ASK A REQUEST
FROM ME TO YOU.
HOW DO YOU SEE THE RESEARCH
COMMUNITY ENGAGING WITH YOUR
WORK THAT YOU PRESENTED TODAY
AND HOW DO YOU SEE THEM REUSING
YOUR DATA TO CREATE NEW RESEARCH
FINDINGS AND HYPOTHESIS?
ANDREIA.
>>YEAH.
I THINK FOR INSTANCE WITH OUR --
WE SHARE THESE TOOLS PUBLICLY AS
YOU SAW AND THEN EVERY YEAR
RECEIVE DOZENS OF IMAGES FROM
PEOPLE AROUND THE WORLD THAT
WANT TO USE THE RESOURCES OR MAY
BE ASKING QUESTIONS.
AND FOR -- I THINK IT'S
DIFFICULT FOR SOME MODELS BUT
FOR DISEASE LIKE STROKE OR
HEALTHY BRAIN -- EVERYONE HAS
HIS OR HER LITTLE DATASET THAT
THEY WOULD BE VERY GLAD TO SHARE
IF THEY CAN COUNT WITH THE
FRUITS OF SHARING.
SO I THINK ONE BIG POINT IS THAT
IF YOU CAN HAVE THIS ORGANIZED
REPOSITORY THAT ALLOW NOT JUST
DOWNLOADING BUT INPUTS AND ALSO
WORK FOR LIFE BECAUSE THEY HAVE
QUALITY CONTROL THAT IS THERE
FOR -- LIFE.
BUT PEOPLE WOULD BE MORE THAN
GLAD TO DEVELOP THIS NEW MODEL
OF RESEARCH.
THIS COLLABORATIVE MODEL OF
RESEARCH THAT ACCELERATES THE
RESEARCH FOR EVERYONE INVOLVED.
>>THANK YOU SO MUCH.
JAKOB, WHAT DO YOU THINK.
HOW CAN RESEARCHERS ENGAGE WITH
YOU AND YOUR WORK AND PROMOTE
DATA REUSE IN THEIR OWN
RESEARCH.
>>I THINK THE BIGGEST THING IS
TO GIVE FEEDBACK.
FROM THE RESOURCE THAT WE BUILT
GIVE FEED BAD.
WE'RE HERE AND DOING OUR
SCIENCE.
WE WANT TO MAKE THIS A
SUSTAINABLE RESOURCE WITH THE
RESOURCE THAT'S WE HAVE
INTERNALLY.
PEOPLE HAVE E-MAILED US TO HELP
OUT.
PEOPLE WANT TO SHARE DATA. THAT
IS THE BIGGEST LEARNING PROCESS.
I CULLED HUNDREDS OF E-MAIL.
AND THEY ALL WANTED TO SHARE
DATA.
THE POINT BEING IS INTERACT-AND
COMMUNICATE WITH US AND WE CAN
HELP FACILITATE OTHER RESOURCES.
THIS IS THE FIRST STEP ALWAYS IN
A LONGER HOPEFULLY MORE
SUSTAINABLE INFRASTRUCTURE.
AND THEN I GUESS THE OTHER THING
IS WHAT WE DID OR WE TRIED TO DO
IS WHEN WE COULD NOT SHARE THE
DATA WE TRIED TO PUT THE LINKS
TO THE RESOURCES.
SEARCHING ON-LINE TAKES FOREVER
AND SOMETIMES THE ACCESS
PROTOCOLS EVEN IF IT'S OPEN DATA
IT'S A HUGE BURDEN SO WE CAN
HELP FACILITATE ANSWERING
QUESTIONS ABOUT HOW WE GOT
ACCESS TO CERTAIN DATASETS.
OR HOW TO USE THE DATA THAT IS
AVAILABLE.
SO BASICALLY BACK TO THE FIRST
SIMPLE POINT IS JUST
COMMUNICATE.
THAT IS THE BEST THING IS MAKE
US AWARE OF SOMETHING THAT'S AN
ISSUE OR WHAT LIKE OR DON'T LIKE
AND WHAT YOU WANT TO SEE
IMPROVED.
THAT IS THE BEST WAY FOR US TO
BE ABLE TO LEARN.
>>THAT IS WONDERFUL.
I WANT TO COMMEND BOTH OF YOU ON
THE WORK THAT YOU'VE DONE.
THESE ARE INCREDIBLE TALKS.
AND ALSO INCREDIBLE RESOURCES TO
USE TO UNDERSTAND IMAGING.
AND BRAIN FUNCTION.
THE LAST QUESTION IS A QUESTION
FOR NIH AND FASEB.
IS WHAT IS THE NEXT CHALLENGE?
I'M HAPPY TO TELL YOU THAT THIS
MORNING WE'VE APPROVAL FROM OUR
LEGAL SIDE AT NIH TO CONTINUE
WITH THIS CHALLENGE SO WE'RE
GOING THROUGH THE FINAL PROCESS
OF GETTING THIS PARTNERSHIP
CONTINUATION.
WE'VE APPROVAL FROM OUR LEGAL
TEAM SO YEAH!
I WANT TO INTRO DUE MY COLLEAGUE
WHO IS LEADING US AT NIH ON THE
PARTNERSHIP WITH FASEB AND DATA
WORKS.
IF YOU'RE THERE COME ON AND
PLEASE SAY A FEW WORDS ABOUT THE
CHALLENGE AND YOUR VISION.
I SHOULD HAVE INTRODUCED YOU AT
THE BEGINNING.
>>THANK YOU FOR THE OPPORTUNITY
AND THIS IS SUPER EXCITING TO
HEAR BOTH JAKOB AND ANDREIA TO
SHARE.
TO GIVE OPPORTUNITIES FOR MORE
PEOPLE IN THE COMMUNITY.
THE MOST IMPORTANT LESSON IS TO
GO FROM BIG LARGE DATA PROJECTS
ALL THE WAY THROUGH TO OUR
INDIVIDUAL RESEARCHERS AND RO1
GRANTEES AND HOW THEY CAN BE
ENABLED.
HOW CAN WE PROVIDE TOOLS AND
RESOURCE THAT'S ARE THERE THAT
ENABLE EVERY PERSON IN THE
LARGER RESEARCH LANDSCAPE TO
ENGAGE WITH THIS.
THAT IS THE FOCUS NEXT YEAR AND
WE'RE HOPING THAT FOLKS WILL
COME UP NOT ONLY WITH THEIR
EXCITING STORIES AND NEW CASES
BUT ALSO NICE RECIPES AS WE ARE
CALLING IT ANTIDOTALLY THAT
PEOPLE CAN USE BY THE LARGER
COMMUNITY.
>>THANK YOU SO MUCH.
AND THANK YOU EVERYONE.
IT'S NOW MY PLEASURE TO
INTRODUCE THE MODERATOR FOR OUR
NEXT PANEL DR. SHEENAH MASAHIRO
TANAKA THE ASSOCIATE PROFESSOR
AT NYU SCHOOL OF MEDICINE.
SO THANK YOU SO MUCH FOR JOINING
US TODAY AND MODERATING OUR NEXT
PANEL AND OUR NEXT PANEL IS ON
INTEGRATION TECHNOLOGIES TO
ADVANCE HUMAN HEALTH.
>>THANK YOU VERY MUCH, SUSAN
FOR THAT NICE INTRODUCTION AND
HELLO, EVERYONE AND WELCOME TO
OUR PANELISTS WHICH ARE WE'RE
GOING TO BE DOING THE DATA
INTEGRATION TECHNOLOGIES FOR
ADVANCING HUMAN HEALTH.
AND I WANT TO INTRODUCE OUR TWO
PANELISTS.
OMAR HARB FROM THE UNIVERSITY OF
PENNSYLVANIA.
AND MICHAEL HAWRYLYCZ FROM THE
ALLEN INSTITUTE FOR BRAIN
SCIENCE.
I KNOW THIS IS GOING TO BE AN
INCREDIBLY INTERESTING PANEL.
THESE ARE TWO AWARDS THAT WERE
GIVEN FOR TEAM EFFORTS IN TERMS
OF DATA REUSE AND THIS IS A HUGE
AREA OF IMPORTANCE FOR THE
BIOINFORMATICS AND THE
SCIENTIFIC COMMUNITY.
SO I WELCOME BOTH OF YOU AND NOW
I WILL INTRODUCE OMAR WHO WILL
BE OUR FIRST SPEAKER AND OMAR IS
GOING TO BE TELLING US ABOUT THE
PATHOGEN VECTOR AND HOST
INFORMATICS RESOURCE WHICH
PROVIDES A WONDERFUL INTEGRATION
OF DATA.
THIS IS A LARGE TEAM.
I MENTIONED THAT THERE WERE
ALMOST 60 PEOPLE INVOLVED IN
THIS.
VERY INTERESTED TO HEAR ABOUT
THIS.
THANK YOU.
>>GREAT.
THANK YOU, SHEENAH.
IT'S GREAT TO BE HERE.
AND AS INDICATED I'M
REPRESENTING A LARGE TEAM.
I WAS FORTUNATE TO HAVE A NUMBER
OF TEAM MEMBERS ON THE DATA
WORKS CHALLENGE.
THOSE INCLUDE DAVID BRUCE AND
MARY ANN DOWEL.
SO BUILDING ON THE PREVIOUS
PANEL IT'S ACTUALLY I HAVE TO
SAY IN THE WORLD OF GENOMICS
WE'VE BEEN A LITTLE BIT
FORTUNATE MAYBE A LITTLE BIT
SPOILED THAT NIAID HAD A VISION
ALMOST 20 YEARS AGO TO SUPPORT
THIS.
THEY FUNDED THIS WRE MECHANISM D
WE'VE BEEN FORTUNATE TO BE
SUCCESSFUL IN RECEIVING FUNDING
FOR THIS RESOURCE CENTER TO
SUPPORT ORGANISMS THAT ARE
INCLUDED IN OUR RESOURCE.
THE OTHER THING TO POINT OUT IS
THAT IN THE GENOMIC WORLD THE
IDEA OF DATA SHARING BECAME
ALMOST A REQUIREMENT EARLY ON
WITH THE AVAILABILITY OF
REPOSITORIES.
THOSE ARE SUPER IMPORTANT TO
HAVE STRUCTURED AND WELL
ESTABLISHED REPOSITORIES.
AND WE HAVE SUCH REPOSITORIES.
CURRENTLY THERE ARE TWO CENTERS
FUNDED BY NIAID.
THESE INCLUDE BACTERIAL AND
VIRAL PATHOGENS.
IF YOU WORK ON THESE ORGANISMS
AND I ENCOURAGE YOU TO VISIT.
SO THE ORGANISMS THAT WE SUPPORT
STAND DIFFERENT ASPECTS OF THE
TREE OF LIFE INCLUDING MANY THAT
CAUSE DISEASE -- IN HUMANS SUCH
AS -- MALARIA.
AND FUNGAL ORGANISMS AND MANY
OTHER PARASITIC ORGANISMS.
AND ALSO VECTORS OF THOSE
ORGANISMS SUCH AS MOSQUITOES AND
TICKS.
THESE DISEASES TOGETHER CAUSE
MILLIONS OF DEATHS AND SICKNESS
ALL OVER THE WORLD AND MANY ARE
ALSO IMPORTANT NOT ONLY IN THE
GLOBAL SOUTH BUT ALSO IN
NORTHERN AND WESTERN COUNTRIES
INCLUDING THINGS LIKE -- A
PATHOGEN IN THE NORTHEAST
CARRIED BY TICKS.
AND MANY OTHER FUNGAL ORGANISMS
SUCH AS CANADA WHICH IS A
IMPORTANT -- DISEASE.
THEY ARE BASICALLY FILTERS ON
THE SITE FILTERED AND TAILORED
TO THE VARIOUS COMMITTEES THAT
WE SUPPORT.
SO FOR EXAMPLE IT'S FILTERED TO
INCLUDE THESE ORGANISMS.
SO OUR GOAL IS TO INTEGRATE DATA
BIG DATA.
AND SHOWING YOU JUST A LISTING.
AND THE GOAL IS NOT TO SIMPLY BE
A REPOSITORY BECAUSE THERE ARE
SOME REALLY GOOD REPOSITORIES
OUT THERE BUT OUR GOAL IS TO
PROVIDE THE SCIENTISTS OUR
CLIENTS WITH A MECHANISM TO
INTERROGATE THIS DATA IN A WAY
THAT IS MEANINGFUL TO THEM AND
ALLOWS THEM TO DEVELOP NEW
HYPOTHESIS.
METADATA IS ALMOST A BAD WORD
BECAUSE IT'S REALLY DATA.
DATA ABOUT DATA AND THERE IS A
LOT OF IT AND IT NEEDS TO BE
STRUCTURED AND PROPERLY
INTEGRATED.
AND WE USE ONCOLOGY TO STRUCTURE
AND DID DESCRIBE THIS DATA AND
INTEGRATE IT INTO STRUCTURED
DATABASES THAT WE CAN THEN QUERY
AND GET INFORMATION FROM.
AN IMPORTANT ASPECT IS COMMUNITY
ENGAGEMENT AND THIS ENGAGEMENT
ALLOWS US TO IDENTIFY IMPORTANT
DATASETS, PRIORITIZE THEM FOR
LOADING.
A BIG PORTION OF BEING ABLE TO
PRESENT AND PROVIDE DATA
EFFECTIVELY IS TO PROPERLY
DESCRIBE IT.
THE METADATA AND MAKE SURE THE
SAMPLES ARE PROPERLY DESCRIBED.
SO THAT QUERIES CAN BE PERFORMED
AGAINST THESE DATASETS SO THAT
PROPER INFORMATION CAN BE
ACCESSED.
IN ADDITION TO PROVIDING THE
DESCRIPTIONS OF THE DATA AND
INTEGRATING IT AND PROVIDING
TOOLS TO ANALYZE THIS DATA WE GO
THROUGH A RIGOROUS PROCESS OF
QUALITY CONTROL.
THIS IS SHOWING YOU A SNIPPET AT
THE END OF LAST YEAR.
THREE RELEASES AND EACH INCLUDES
EVERY ASPECT THAT IS SHOWN HERE
ON THE LEFT INCLUDING THE FINAL
QA OF THE DATASETS.
IN ORDER FOR US TO BE EFFECTIVE
WE HAVE TO BUILD COMMUNITY TRUST
AND WE DO THIS THROUGH MANY
WAYS.
SOME OF THE ASPECTS IS FIRST OF
ALL MAKING SURE THAT WE'RE
ACCURATING DESCRIBING THE DATA.
MAKING SURE WE COMMUNICATE WITH
PROVIDERS OF DATA AND THE
DATASETS THAT WE LOAD EVEN
PUBLISHED LATE A WE COMMUNICATE
TO MAKE SURE THEY ARE PARTNERS
WITH US AS WE LOAD AND INTEGRATE
THE DATA. AND THE QA PROCESS IS
RIGOROUS AND THAT ALSO INCLUDES
THE COMMUNITY.
THAT OUR COMMUNITY PARTNERS ARE
QAING THE DATA TO MAKE SURE THEY
ARE HAPPY WITH THE RESULTS.
THE IDEA IS TO MAKE SURE THAT WE
HAVE A TRUSTED RELATIONSHIP WITH
OUR COMMUNITY.
ALL OF THESE ARE IMPORTANT AND
OBVIOUSLY ALLOWS YOU TO THEN
FIND THE DATA.
YOU CAN GO IN AND QUERY AND FIND
DATASETS BASED ON KEY WORDS.
THIS IS ALL WELL AND GOOD.
BUT THIS IS ALMOST USELESS IF
YOU CAN KNOT SEARCH EFFECTIVELY
AND INTEGRATE IT.
AND FOR THIS WHAT OUR TEAM HAS
DONE IS WE'VE INTEGRATED THIS
DATA IN USING STRUCTURED
SEARCHES USING WEB INTERFACES
AND THE IDEA IS THE STANDARD
SCIENTIST JUST LIKE ME, I WORKED
MOST OF MY ADULT LIFE IN A LAB.
AND I NEVER KNEW ANYTHING ABOUT
COMPUTATIONAL BIOLOGY AND OUR
PHILOSOPHY IS MAKING SURE THAT
IT'S ACCESSIBLE.
TO LOOK FOR A PARTICULAR DATA
TYPE OR PARTICULAR SEARCH.
AND THEN RUN THE SEARCHES.
HOWEVER, AN ADDED LAYER AND THIS
IS A HUGE INNOVATION -- THAT IS
BUILDING SOMETHING CALLED A
SEARCH STRATEGY.
THIS IS A MECHANISM WHERE YOU
CAN RUN VARIOUS SEARCHES AND
INTEGRATE THEM TOGETHER TO COME
UP WITH LISTS OF RESULTS AND IN
THIS CASE GENES THAT ALLOW YOU
TO BUILD NOVEL HYPOTHESIS AND GO
BACK TO THE LAB AND TEST THEM.
ONE THAT YOU'RE INTEGRATING
RESULTS FROM DIFFERENT
EXPERIMENTS AND THE SECOND IS
THAT YOU'RE INTEGRATING
DIFFERENT TYPES OF RESULTS.
SO WE'RE INTEGRATING RESULTS
FROM AN ANALYSIS ON A GENOME.
CRISPR SCREENS.
YOUR OWN ANALYZED DATA AND
ADDING HERE POST TRANSITIONAL
MODIFICATION SEARCH.
WE HAVE A HUGE LIST OF YouTube
TUTORIALS.
THE WAY THIS WORKS IS SHOWING
YOU HERE.
YOU GO A FIND THE SEARCH THAT
YOU'RE INTERESTED IN.
RUN YOUR FIRST SEARCH.
SELECT THE ORGANISM OF INTEREST
AND THEN START YOUR FIRST
STRATEGY WHICH INCLUDES THE
FIRST STEP.
AND THEN YOU ADD STEPS AND
SEARCH FOR ADDITIONAL SEARCHES
RIGHT HERE.
YOU DECIDE HOW YOU'RE GOING TO
JOIN WITH EACH OTHER AND WITH
DIFFERENT OPERATIONS AND AS YOU
BUILD THIS STRATEGY YOU KEEP
GOING AND GOING AND YOU BUILD
THIS INTO AN EXPERIMENT THAT
GIVES YOU RESULTS THAT YOU ARE
GO BACK AND TEST AND YOU CAN
LOOK AT THESE RESULTS AND
ANALYZE THEM DIFFERENT WAYS AND
TAKE THEM A STEP FURTHER.
W IF YOU WANT TO BROWSE OR LOOK
AT EXPRESSION PROFILES OR
PHENOTYPE PROFILES FOR A GENOME
OR LOOKING AT THE LOCATION OF
VARIOUS ISOLATES.
IN THIS CASE LOOKING AT
MOSQUITOES IN DIFFERENT PARTS OF
THE WORLD AND WHETHER THEY ARE
INFECTED OR NOT.
YOU CAN LOOK AT A MAP THAT IS
EXPLORABLE WHICH IS REALLY COOL.
AND OF COURSE IT'S NICE TO BE
ABLE TO SEE THAT DATABASES ARE
USED AND WE HAVE -- WE'RE ACTIVE
ON VARIOUS SOCIAL MEDIA TO STAY
IN TOUCH WITH OUR COMMUNITIES.
WE ALSO PROVIDE TOOLS TO ANALYZE
YOUR OWN DATA AND INTEGRATE IT.
WE TAKE ADVANTAGE OF A TOOL
CALLED GALAXY.
WHICH ALLOWS YOU TO UPLOAD YOUR
OWN DATA.
AGAIN USER INTERFACE.
AND TAKE THE RESULTS AND BRING
THEM BACK IN AND GO ON
ENRICHMENTS OR RUN STRUCTURED
SEARCHES.
I WANT TO GO BACK TO THE
COMMUNITY ENGAGEMENT.
LISTENING TO THE COMMUNITY IS
IMPORTANT.
THIS ALLOWS US TO KNOW WHAT IS
COMING.
WHAT DATASETS ARE IMPORTANT.
BECAUSE MAKING SURE THAT THESE
DATASETS ARE AVAILABLE AS SOON
AS POSSIBLE MAKES THEM, GIVES
THEM A LONGER SHELF LIFE.
IMAGINE THE RETURN ON
INVESTMENT.
WE HAVE DATASETS THAT HAVE BEEN
LOADED IN OUR DATABASES ALMOST
20 YEARS AGO AND THEY ARE STILL
BEING USED.
THIS IS HUGE.
SO I THINK MORE OF THAT IS SUPER
IMPORTANT.
WE HEAR FROM OUR COMMUNITY.
THEY VIEW US AS PARTNERS.
WE VIEW THEM AS PARTNERS AS WELL
AND THEY TELL US WHEN WE'RE
DOING SOMETHING WRONG OR RIGHT
AND THEY GIVE US ADVICE ON WHAT
FEATURES THEY WANT.
WE HAVE A STRONG EDUCATIONAL ARM
WHERE WE RUN BOTH IN PERSON AND
VIRTUAL WORKSHOPS AND MULTIPLE
YouTube AND WEBINAR.
SO I RECOMMEND TO GO TO OUR
YouTube CHANNEL TO EXPLORE OUR
WORKSHOPS.
WE RECORD EVERYTHING.
HERE IS A SNIPPET.
AND YOU CAN SEE THAT WE HAVE A
GOOD REPRESENTATION ACROSS THE
WORLD AND HERE IS AN EXAMPLE.
WE HAVE ACCESS FROM ALL OVER THE
WORLD DURING THE WORKSHOP WHICH
IS REALLY GREAT AND NICE TO SEE.
YOU WANT TO MAKE SURE THAT YOUR
DATABASE IS BEING USED AND IT'S
NICE TO SEE THE CITATIONS.
SO I'M SHOWING A SNIPPET.
OUR CITATIONS ARE GOING UP AND
WE'RE UP TO ABOUT 2500 CITATIONS
ANNUALLY.
SO, THAT IS VERY GOOD AND MANY
ARE QUITE NICE TO READ BECAUSE
THEY ARE HEAVILY USED RESOURCE
TO DRIVE THE HYPOTHESIS THAT
THEY ARE TESTING THEM IN THE
PAPERS AND GENERATING NEW DATA
AND COMES BACK AND CONTRIBUTES
TO THE RESOURCE.
THIS WILL ITERATE THAT THIS
MECHANISM OF DATA DATASETS FROM
REPOSITORIES OR DIRECTLY FROM
SCIENTISTS TAKING THEM INTO A
STRUCTURED REPOSITORY WHERE YOU
CAN DESCRIBE THE DATA AND PUT
MAKE SURE IT'S QUALITY ASSURED
AND BUILDING A USER INTERFACE TO
MINE AND CARY CARE -- CA-QUERY.
*.
THIS HAS MADE US INTO A TRUSTED
RESOURCE AND MAKES IT USEFUL FOR
EVERYONE.
I'LL CONCLUDE AGAIN BY
HIGHLIGHTING MY TEAMMATES WHO
HAVE BEEN INSTRUMENTAL AND
HIGHLIGHTING THE SOFTWARE
ENGINEERS AND OTHER SCIENTISTS
WHO HAVE BEEN WORKING WITH US.
MOST OF THE PEOPLE OF ARE, THEIR
JOB IS TO WORK FOR THESE
RESOURCES.
THEY ARE NOT POSTDOCS OR
GRADUATE STUDENTS AND THAT GIVES
US A NICE EDGE AND ADDRESSES ONE
OF THE QUESTIONS THAT THERE
NEEDS TO BE THIS SUSTAINED
FUNDING FOR RESOURCES THAT
REALLY MAKE THE DATA ACCESSIBLE,
REPOSITORY IS IMPORTANT AND
ESSENTIAL BUT THE DATA NEEDS TO
BE ACCESSIBLE IN A WAY THAT IT
CAN BE INTERROGATED AND MADE
USEFUL FOR HYPOTHESIS
GENERATION.
I'LL END HERE.
THANK YOU VERY MUCH.
>>THANK YOU VERY MUCH OMAR.
THIS WAS TERRIFIC.
AND I KNOW THAT THERE WILL BE
MANY QUESTIONS BUT WE'RE GOING
TO HEAR FROM OUR NEXT SPEAKER
AGAIN.
THIS IS TRULY A WONDER.
EXAMPLE OF A GLOBAL RESOURCE.
AND NEXT WE'RE GOING TO HEAR
FROM MICHAEL HAWRYLYCZ FROM THE
ALLEN INSTITUTE FOR BRAIN
SCIENCE.
ABOUT THE CELL TYPE KNOWLEDGE
EXPLORER.
AGAIN A PUBLIC ACCESS ATLAS FOR
MULTI-MODEL SUBTYPE DATA.
THIS IS A REALLY FAST-MOVING
AREA PARTICULARLY IN TERMS OF
SINGLE CELL WORK.
AND VERY INTERESTED TO HEAR FROM
MICHAEL.
SO PLEASE, TAKE IT AWAY.
>>YOU CAN SEE MY PRESENTATION.
>>YES.
WE CAN
>>THANK YOU.
I'D LIKE TO THANK THE F FASEB AH
-- I'M GOING TO TALK ABOUT THIS
TOOL CALLED THE CELL TYPE
KNOWLEDGE EXPLORER BUT I'M ALSO
GOING TO TALK ABOUT HOW IT
INTERFACES WITH OTHER
TECHNOLOGIES THAT WE HAVE IN
TERMS OF DATA INTEGRATION FOR
HUMAN HEALTH.
SO A MAJOR KIND OF INITIATIVE OF
THE BRAIN INITIATIVE WAS THIS
BIG CONSORTIUM CALLED THE BRA --
BRAIN INITIATIVE CELL NETWORK.
THIS WAS A LARGE CONSORTIUM
EFFORT TO PROFILE ALL OF THE
CELL TYPES OF THE MOUSE BRAIN
WITH PROTOTYPES DEVELOPED FOR
THE HUMAN AND THE ALSO NONHUMAN
PRIMATE.
IT RESULTED IN THE END IN REALLY
A BRAIN WIDE PROFILING OF THE
MOUSE BRAIN AND A DEEP DIVE INTO
SEVERAL AREAS.
IN 2021 THERE WAS A LARGE
ORCHESTRATED EFFORT OF SOME
OTHER PARTICIPANTS PLAYED AN
INTEGRAL PART INVOLVED IN
CHARACTERIZING THESE CELL TYPES
IN THE PRIMARY MOTOR CORTEX
ACROSS SPECIES.
SO THIS WAS A -- USING
TECHNOLOGY OF SINGLE CELL DATA
SPATIAL -- AND TECHNIQUES.
EPIGENETIC TECHNIQUES.
USE OF TRANSGENIC LINES IN TERMS
OF CHARACTERIZING AND DESCRIBING
THE RELATIONSHIPS AMONG THESE
CELL TYPES.
IT RESULTED IN AN ENTIRE ISSUE
OF NATURE IN OCTOBER OF 2021.
A RESULT OF THIS IS REALLY A
TOXONOMY OF CELL TYPES INITIALLY
IN THIS AREA IN THE PRIMARY
MORTAR CORTEX.
IT HAS BEEN EXTENDED NOW TO THE
ENTIRE BRAIN ALTHOUGH THE
ANNOTATION OF THAT IS STILL A
WORK IN PROGRESS.
BUT THIS PRESENTS THE TAXONOMY
IN THE MOTOR CORTEX SHOWING THE
DIFFERENT CELL TYPES FROM
NEURONAL TO NONNEURONAL, THE
CLASSICAL SUBTYPES THAT WE SEE.
AND WE ARE ABLE TO ACTUALLY LOOK
AT AND UNDERSTAND THE PROFILING
OF THESE NEURONS.
VARIOUS ASPECTS OF THEIR FIRING
PATTERNS AND OTHER ASPECTS.
ONE OF THE THINGS THAT WE DID AS
PART OF THIS WHICH WE RECEIVED
THE AWARD FOR WAS BUILDING OF
THIS SOFTWARE WHICH WE CALL THE
CELL TECHNICAL EXPLORER.
WHICH CAPTURES THE WORK AND THE
PROFILING OF THE PRIMARY MOTOR
CORTEX.
EMBEDS THE DATA ACROSS THE
SPECIES AND ALLOWS USERS TO
BROWSE THIS DATA BY CELL TYPES
TO LINK BETWEEN THEM TO DO A
DEEP PROFILING AND DEEP LOOK AT
THE INDIVIDUAL CELL TYPES AND
INVESTIGATE THEIR
CHARACTERISTICS AS WELL AS TO
MAP DATA USING THE TOOL TO MAP
THEIR OWN DATA TO THE CELL TYPES
TO SEE FOR EXAMPLE WHAT TYPES
THEIR DATA MIGHT BE
REPRESENTATIVE OF.
WE CAN SEE THIS ALIGNMENT.
NOT ALL CELL TYPES -- THERE ARE
SPECIFICS TO THE MARMOSET, HUMAN
AND THE MOUSE BUT THERE IS A
COMMON CORSET WHICH ALIGNS
BETWEEN THEM AND WE'RE ABLE TO
BRING THAT OUT.
IN THIS CELL TECHNOLOGY EXPLORER
IT'S A FIRST OF ITS KIND THING.
IT WAS MOTIVATED BY THIS OPEN
RESOURCE CALLED GENE CARDS WHICH
I AM'S SURE MANY PEOPLE ARE
FAMILIAR WITH.
BUT HERE WE ACTUALLY INCORPORATE
THESE MODAL TEASE AND LINKAGES.
AND IT COMES WITH AN ONTOLOGY
WHICH EXPLAINS.
AND USERS CAN CROSS CORRELATE
AND STUDY THE INDIVIDUAL TYPES.
I WANT TO TALK ABOUT HOW THIS
SITS IN THE BIGGER SORT OF OUR
BIGGER DATA ECOSYSTEM AND ITS
IMPLICATIONS.
THESE HAVE REVOLUTIONIZED BOTH
BASIC SCIENCE IN TERMS OF BEING
ABLE TO DEVELOP THESE
CLASSIFICATIONS AS WELL AS NEW
TOOLS FOR SELECTIVE MANIPULATION
OF THESE TYPES THROUGH VIRAL AND
OTHER TYPES OF TECHNOLOGIES BUT
ALSO HAS VERY PROFOUND DISEASE
APPLICATIONS IN TERMS OF
CHARACTERIZING PATHOLOGICAL CELL
TYPE STATES AND TARGET TOOLS.
IN A STUDY THAT WE JUST
PUBLISHED THIS PAST WEEK WE
COMPARED A CONVENTION,
CLASSIFICATION OF DISEASES FROM
THE INTERNATIONAL HEALTH METRIC
ORGANIZATION, A SEATTLE BASED
ORGANIZATION WHICH IS A GLOBAL
CHARACTERIZING OF THE RISK
PROFILING.
AND COMPARING IT IN A CELL TYPE
WAY.
THIS IS A SET OF 24 RAIN
DISEASES WHICH ARE HAVING ABOVE
AVERAGE OF CONCENTRATION OF GENE
EXPRESSION IN THE CORTEX AND
VIEWING THEIR DISEASE RISK GENES
ACROSS THESE DIFFERENT CELL
TYPES AND WE CAN SEE THERE IS
INHERENT GRADIENTS WHICH ARE
REPRODUCIBLE AND EXPLAINABLE.
AND THE WAY THIS PLAYS INTO THIS
KNOWLEDGE TYPE EXPLORER IS THAT
WE CAN USE THESE ALIGNMENTS AND
LOOKING AT THESE CROSS CONSENSUS
TAXONOMIES TO LEVERAGE THESE
THINGS.
IN PARTICULAR HERE I'M USING
THIS MAPPING BETWEEN MOUSE AND
HUMAN TO COMPARE A DISEASE
SIGNATURE AND HOW THEY COMPARE
BETWEEN MOUSE AND HUMAN.
IN THIS MIDDLE PLOT WE'RE
LOOKING AT A TYPE OF CELL TYPE
ENRICHMENT
IS THE CELL TYPE ENRICHED FOR
EXPRESSION IN ANY KIND OF
CONCENTRATION.
AND THE ORANGE REPRESENTS MOUSE
FOR THE DISEASE AND THE BLUE IS
THE SAME DISEASE IN HUMAN AND WE
CAN SEE THERE IS A REMARKABLE
KIND OF PAIRING OF THESE THAT
MOUSE AND HUMAN.
THIS IS JUST A DATA DRIVEN
PRESENTATION BUT MOUSE AND HUMAN
ARE ALL KIND OF CLUSTERING
TOGETHER FOR A LOT OF THESE
DISEASES INDICATING THAT IT'S
THE SAME CELL TYPES INVOLVED IN
THESE DISEASES.
WHEREAS ON THE RIGHT WHEN WE
LOOK AT THE EXPRESS LEVEL WE SEE
A SPECIES SPECIAL GROUPING AND
THIS IS INDICATIVE OF DIFFERENT
GREEDIFFER --DIFFERENT GENES IN.
ANOTHER IS THIS PROJECT CALLED
SEATTLE ALZHEIMER'S DISEASE
BRAIN ANALYSIS.
THIS IS A PARTNERSHIP WITH THE
UNIVERSITY OF WASHINGTON AND
KAISER PERMANENTE -- THAT HAVE
COME IN THROUGH THE SYSTEM WHO
HAVE DEVELOPED AN ALZHEIMER'S
DISEASE WHERE WE HAVE EXTENSIVE
METADATA AND DO A GENOMIC
PROFILING OF.
AND SO THIS DATA HAS BEEN
RELEASED.
IT LINKS BACK TO THE EXPLORER.
IT COMES WITH THE ABILITY TO
LOOK AT THE DATA BY CELL TYPE.
TO LOOK AT THE NEURO PATHOLOGY
IMAGES.
THERE IS A WAY OF LOOKING AT
INDIVIDUAL DONORS WITH DISEASE
SEVERITY AND COGNITIVE STATUS BY
CELL TYPE AND PROJECTING THAT ON
TO THE NORMAL STATUS OF
TOXONOMY.
ONE CAN VIEW THE NEURAL
PATHOLOGY AND NOW MAP DATE TABAK
TO THE TAXONOMY SUCH AS IS DONE
IN THE EXMORER.
EXPLORER.
THE IMPORTANCE * IS ONE OF
COMBINING DATA FROM MULTIPLE
SOURCES AT A CONSORTIUM LEVEL
INTO BUILDING TOOLS THAT PROVIDE
KNOWLEDGE STRUCTURES FOR REALLY
LEVERAGING INFORMATION.
YOU KNOW I THINK THE DATA
SCIENCE SUFFERS IN MANY WAYS
FROM A LOT OF CHARACTERISTICS
WHICH I'M SURE EVERYBODY ON
THESE PANELS HAVE A COMMON KIND
OF EXPERIENCES.
WE -- OUR EXPERIENCE OF
KNOWLEDGE CAPTURE IS TO SOME
EXTEEX-- EXTENT PubMed.
AND I THINK THERE ARE MANY
INITIATIVE US UNDERWAY TO TRY TO
MAKE SOME PROGRESS THERE.
SO, THAT IS ALL I WANT TO SAY.
I WANT TO THANK OUR TEAMMATES
AND COLLABORATORS ACROSS THE
CONSORTIUM.
THANK YOU.
>>THANK YOU, MICHAEL.
THAT WAS TERRIFIC.
AND WE HAVE NOW HEARD FROM OUR
TWO PANELISTS FOR THIS SESSION.
DATA REUSE.
AND PARTICULARLY DATA REUSE IN
ADVANCING HUMAN HEALTH.
SO I WOULD LIKE TO NOW OPEN UP
TO QUESTIONS.
I WOULD ENCOURAGE ANYONE TO ASK
QUESTIONS ON SLIDO.
GO TO SLIDO.com AND ENTER 1379
AND THAT WILL GIVE THE QUESTIONS
TO THIS SESSION.
I'M GOING TO START WITH A
QUESTION ACTUALLY FROM THE
AUDIENCE TO OMAR.
FROM YOUR OUTREACH TO ENCOURAGE
DATABASE USAGE WHAT ARE SOME OF
THE WAYS THAT YOU HAVE BEEN ABLE
TO GENERATE ENGAGEMENT AROUND
YOUR WORK TO THE COMMUNITY?
>>THAT IS A GOOD QUESTION.
OBVIOUSLY A PROCESS.
OUTREACH IS A PROCESS.
YOU DON'T JUST SHOW UP AND DO
ONE THING.
IT'S A PROCESS.
IT INVOLVES ENSURING THAT THE
COMMUNITY FEELS AS A PARTNER.
I CANNOT STRESS THAT TOO MUCH.
AND ANY CONFERENCE WE ATTEND.
ANY WORKSHOP THAT WE RUN.
ANY WEBINAR WE PERFORM WE
CONDUCT OUR GOAL ALWAYS IS TO
MAKE SURE THE COMMUNITY IS AWARE
THAT THEY CAN GIVE US FEEDBACK
AND TELL US WHAT THEY NEED.
WHEN WE BUILD -- THE RESOURCES
OUR GOAL WAS NOT TO BUILD TOOLS
FOR OURSELVES.
I'M NOT DOING RESEARCH.
WHAT I'M TRYING TO MAKE SURE IS
THAT THE TOOLS THAT WE'RE
BUILDING ARE USE.
TO THE USERS SO THEY CAN DO THE
RESEARCH.
ANY TIME WE'RE AT CONFERENCES WE
MAKE SURE WE HAVE HELP DESK.
DESIGNED TO BE AND TO PROVIDE
TUTORIALS.
MAKE US VERY ACCESSIBLE.
WE HAVE A VERY ACTIVE HELP LINE
ON-LINE.
SO WE GET I THINK CLOSE TO 20
E-MAILS A WEEK ASKING US
ANYTHING FROM HOW CAN WE ANALYZE
A CERTAIN DATASET TO TELL US
ABOUT NEW DATA AND NEW
FUNCTIONS.
OR LET US KNOW ABOUT A BUG ON
THE SITE WHICH IS GREAT TOO.
A LOT OF COMMUNITY ENGAGEMENT.
WE'RE VERY ACTIVE ON SOCIAL
MEDIA.
FACEBOOK AND Twitter.
WE DO TWEET OUT ALL THE TIME
MAKING SHARE THAT WE'RE PART OF
THE 13 HEAR OF SCIENTISTS.
* OPEN ACCESS AND MAKING SURE
THAT WE'RE ACCESSIBLE TO
EVERYBODY.
>>THAT IS A TERRIFIC ANSWER TO
A VERY GOOD QUESTION BECAUSE YOU
REALLY EMPHASIZE THE IMPORTANCE
OF DEVELOPING A RESOURCE THAT IS
SHARED BY THE COMMUNITY.
SO THERE IS CERTAINLY A SENSE A
OWNERSHIP AND PEOPLE OF WANT TO
CONTRIBUTE AND MAKE IT BETTER.
SO I THINK THAT IS REALLY HUGE.
MICHAEL FROM YOUR PERSPECTIVE,
YOU ALSO HAVE A COMMUNITY THAT
IS CONTRIBUTING.
HOW DO YOU ALSO SHARE THAT
KNOWLEDGE WITH YOUR COMMUNITY?
WITH THE NEURO COMMUNITY?
>>I GUESS FOR ONE THING LARGE
SCALE NEURO SITES INITIATIVES
HAVE BECOME INCREASINGLY
COLLABORATIVE.
THERE IS MORE ORGANIZATION.
OUR PROGRAM OFFICERS ARE MUCH
MORE INVOLVED.
WE HAVE A LOT OF -- THERE IS A
LOT OF JOINT WORK AND WORKING
GROUPS AND STANDARDS BOARDS AND
OTHER THINGS FOR PUTTING
INFORMATION FORWARD WHICH TRIES
TO CAPTURE THIS AND PRESENT THIS
IN A WAY.
FURTHER AS A MANDATE OF THE
BRAIN INITIATIVE ALL OF THAT
DATA GOES INTO PUBLIC ARCHIVES
WHICH ARE MAINTAINED AND ARE
ACTIVELY INVOLVED IN THIS DATA
STANDARDIZATION EFFORT.
SO, ITS STILL IN PERFECT AND I
DON'T KNOW THAT * WE ADDRESS
USER'S INTEREST AND CONCERNS AS
WELL AS WE STILL COULD.
BUT THAT IS BASICALLY IT.
>>YEAH AND I WOULD ALSO SAY
THAT YOU'RE STARTING MORE
RECENTLY THAN OMAR AND THE
DATABASE.
SO YOU'VE GOT ROOM TO GROW IN
THAT AREA.
BUT TRULY WE'RE IN AN ERA OF
EMBRACING TEAM SCIENCE AND
COLLABORATION.
AND I THINK BOTH OF YOU HAVE
GIVEN SOME EXCELLENT EXAMPLES OF
HOW THAT IS MANIFESTED.
SO MICHAEL, I'M SORRY OMAR, THE
QUESTION CAME IN SPECIFICALLY
FOR YOU IN TERMS OF HAVE YOU
ENSURED THE QUALITY
DATA AND WHAT IS THE DATA --
ROLE IN THIS?
>>THAT IS A GOOD QUESTION AS
WELL.
ANY TIME YOU REANALYZE
SOMEBODY'S DATA THERE IS A WORRY
-- THAT YOU DO SOMETHING WRONG
AND YOU MISS ANALYZE THE DATA
AND NOW YOU'RE SHOWING ANALYSIS
THAT IS WRONG AND THAT IS
MISLEADING.
YOU MAY DO SOMETHING RIGHT BUT
IT TURNS OUT THAT YOUR ANALYSIS
BASED ON THE NEW TOOLS THAT
YOU'RE USING OR UPDATED TOOLS IS
PROVIDING DIFFERENT RESULTS.
AND DIFFERENT RESULTS CAN BE
DIFFERENT LAYERS.
WHICH ARE COMPLETELY OPPOSITE TO
WHAT THE SCIENTIST FOUND
INITIALLY AND THAT COULD HAPPEN.
OR IN MOST CASES THERE IS SOME
SLIGHT DIFFERENCES AND THAT IS
FINE.
THAT IS WHAT SCIENCE IS ABOUT.
GENE EXPRESSIONS THAT ARE
QUESTIONABLE AND ONE ANALYSIS
THAT TURNED OUT TO BE
SIGNIFICANT.
THE IDEA IS TO PROVIDE THE
USESSERS WITH THE ABILITY TO GO
IN AND TWEAK THE KNOBS AND
CHANGE THE PARAMETERS.
I WANT TO BE MORE RESTRICTIVE OR
LESS RESTRICTIVE.
IN ORDER FOR US TO QUALITY
ASSURE THIS WE DO A COUPLE OF
THINGS.
WE HAVE AUTOMATED QUALITY
ASSURANCE SCRIPTS THAT RUN AND
MAKE SURE THAT THE DATA IS
LOADED UP CORRECTLY.
AND SO RESULTS ARE SHOWING UP
AND WE'RE GETTING NUMBERS.
THEN WE HAVE A SECOND LAYER
WHICH INVOLVES THE OUTREACH TEAM
WHERE THEY LOOKING AT THE DATA
IN THE DATABASE.
THEY RUN A SEARCH AND SEE WHAT
KIND OF RESULTS AND COMPARE THAT
TO WHAT WAS DESCRIBED IN THE
PAPER.
DOES THAT MAKE SENSE?
ARE WE GETTING WEIRD THINGS?
SO THAT HELPS US.
THAT IS OUR FIRST LAYER AND
AFTER THAT WE SEND AN E-MAIL TO
THE SCIENTIST.
WE SEND EVERY PERSON AN E-MAIL
AND LET THEM KNOW THAT YOUR DATA
IS AVAILABLE AND GIVE THEM
ACCESS TO A PASSWORD PROTECTED
SIDE FOR A COUPLE OF WEEKS
BEFORE IT IS RELEASED.
*.
IT'S STREAMLINED.
AND FROM THAT NOT ONLY DO WE GET
FEEDBACK FROM OUR PROVIDERS THE
SCIENTISTS BUT ALSO BUILDS
TRUST.
AND THAT IS THE FEEDBACK LOOP.
7 THEY ARE EXCITED TO COME AND
TALK TO US AND GIVING US DATA.
WE GET A HUGE AMOUNT OF DATA
PREPUBLICATION AND WE HAVE A
STRICT POLICY OF NEVER RELEASING
ANY DATA UNTIL THE PROVIDE ERA
PROVES.
AND AS A RESULT WE HAVE A UNIQUE
POSITION WHERE WE CAN SHOW DATA
PROVIDERS THEIR DATA BEFORE IT
IS PUBLISHED AND THEY SAY THIS
IS GREAT.
LET'S RELEASE IT.
MY DATA IS ALREADY ON THE
BIOARCHIVE.
THEY CAN NOW SEARCH IT AND THE
REVIEWERS CAN GO AND PLAY AROUND
WITH THE DATA AS THEY ARE
REVIEWING THE PAPER.
SO THAT HAS BEEN A HUGE
ADVANTAGE OF BUILDING THIS
COMMUNITY TRUST.
>>WOW!
THAT IS HUGE.
BECAUSE THAT SPEAKS TO THE POWER
OF THE DATA AND IT SPEAKS TO
WHAT WE'RE TRYING TO BUILD HERE.
IN TERMS OF NIH AND FASEB AND
BEING ABLE TO SHARE THE DATA.
THANK YOU FOR SHARING THAT.
IT ALSO SPEAKS TO PEOPLE
RECOGNIZING YOUR COMMUNITY,
RECOGNIZING THE VALUE OF BEING
PART OF THIS.
IT'S BIGGER THAN ANY ONE OF US
AND THAT IS A GREAT THING.
SO, THANK YOU.
IN TERMS OF MICHAEL IN TERMS OF
-- ARE THERE ANY FINANCIAL
REQUIREMENTS IN TERMS OF
ACCESSING THE DATABASE?
THE CELL TYPE DATABASE?
>>NO.
EVERYTHING IS OPEN AND
AVAILABLE.
I WANT TO RAISE ANOTHER POINT IF
I MIGHT.
>>SURE
>>PART OF THE PLIGHT OF DATA
USE IN BIOLOGY AND NEURO
SCIENCES AND BIOLOGY IN GENERAL
IS THAT WE DON'T NECESSARILY
REALLY EFFECTIVELY USE THE DATA
THAT WE HAVE COLLECTED.
RIGHT.
IT'S AS IF OUR FEAR OF A LACK OF
UNDERSTANDING OF THE MECHANISMS
AND THE SYSTEMS WE'RE EXPLORING,
IT'S ALMOST AS IF WE ASSUME THAT
WE DON'T KNOW ANYTHING AND IT'S
REQUIRED TO START AGAIN WITH A
NEW TECHNOLOGY AND NEW DATASET.
AND NEW TECHNOLOGY IS GREAT.
THEY ALLOW PEOPLE TO EXPLORE
DEEPER AND MORE PRECISELY.
8 BUT PART -- OF THE DATA
ENGINEERING AND KNOWLEDGE
SUFFERS BECAUSE IT ENDS UP BEING
LARGELY A KIND OF REPOSITORY OF
DATA THAT IS NOT EFFECTIVELY
USED.
NOT LEVERAGED AND NEW
EXPERIMENTS ARE PROPOSED ALL THE
TIME.
ONE OF THE BIGGEST CHALLENGES
THAT WE CAN TAKE ON IS ATTEMPT
TO STRESS THE VALUE OR HIGHLIGHT
THE VALUE OF EXISTING DATA AND
USE THAT IN LEVERAGING OUR
SCIENTIFIC HYPOTHESIS.
>>I COMPLETELY AGREE AND I'M
SURE THAT MANY OF THE AUDIENCE
WOULD AGREE AS WELL.
IT ALSO GETS TO THE POINT THAT
BOTH OF YOU SPOKE TO WHICH IS
THE IMPORTANT OF ONTOLOGY
WHETHER THIS IS EXHIBITING.
-- EXISTING AND DEVELOPING.
PEOPLE UNDERSTAND ESTIMATE THE
IMPORTANCE OF ONTOLOGY IN TERMS
OF TRULY SHARING THE DATA AND
SPEAKS TO THE IMPORTANCE OF THE
METADATA
>>THE INFRASTRUCTURE --
ALTHOUGH IT'S NOT PAID FOR AND
IT'S NOT PRIORITIZED ONE COULD
IMAGINE A FAR BETTER KNOWLEDGE
ORGANIZATION OF WHAT WE KNOW
WITH RESPECT TO OUR DATA THAN
EXIST TODAY.
YOU GO TO PubMed AND SEE IF
THERE IS A SUPPLEMENTARY DATASET
THAT IS REQUIRED.
BUT THAT IS A FAR CRY FROM BEING
ABLE TO USE AND UNDERSTAND THIS
DATA IN ANY REAL CONTEXT.
>>ABSOLUTELY.
SO MICHAEL, ONE OF THE QUESTIONS
WITH REGARDS TO COMPARISONS THAT
YOU SHOWED BETWEEN NELSON AND
HUMAN -- IS THIS SOMETHING THAT
YOU SEE THAT IS GOING TO BE VERY
IMPORTANT IN TERMS OF GOING
FORWARD WITH SOME OF THE ANIMAL
MODELS DEVELOPED FOR DISEASE?
>>ANOTHER STEPPING-STONE AT
LEAST IN OUR ABILITY TO
UNDERSTAND WHAT IS DIFFERENT AND
WHAT WORKS AND WHAT DOESN'T WORK
WITH RESPECT TO THE DISEASE
MODELS IN MODEL ORGANISMS.
RIGHT.
WE ALWAYS HEAR COMPLAINTS IN
TERMS OF THE FAILURE OF MODELS
AND ALZHEIMER'S, PSYCHIATRIC
DISEASES AND THINGS LIKE THAT.
BUT I THINK THE WAY TO ADDRESS
THAT IS TO UNDERSTAND AND DETAIL
SOME OF THE REGULATORY
DIFFERENCE ESPECIALLY IN THE WAY
GENES INTERACT AND ARE EXPRESSED
AT A CELL TYPE LEVEL.
THAT IS VERY IMPORTANT.
IT'S QUITE REMARKABLE.
NOW WE HAVE REALLY A WHOLE KIND
OF REALLY -- A MAJOR KIND OF
TAXONOMY ACROSS SPECIES.
MANY SPECIES FROM GREAT APES AND
OTHER MAMMALS AND WE SEE COMMON
STRUCTURE AND PATTERNS AND VERY
SPECIES DIFFERENCES THAT CAN BE
LEVERAGED.
THIS IS A REALLY IMPORTANT
DIRECTION PROBABLY FOR DECIDING
AND UNDERSTANDING UNIQUE HUMAN
ISSUES.
>>YEAH.
THANK YOU.
ANOTHER QUESTION INTERESTING
POINT AROUND KNOWLEDGE CAPTURE.
WHERE DO YOU SEE THIS MOVING
FORWARD AND WHAT INFRASTRUCTURE
IS MOST IMPACTFUL IN
ACCELERATING KNOWLEDGE CAPTURE.
DO ONE OF YOU WANT TO TAKE THAT?
>>SO I THINK OBVIOUSLY HAVING A
MECHANISM TO CAPTURE THE
COMMUNITY'S KNOWLEDGE THE
SCIENTISTS THAT ARE USING THESE
RESOURCES.
IT IS ALSO A FREE RESOURCE.
HAVING A WAY TO BUILD ON WHAT WE
CAPTURED FROM PUBLICATIONS OR
FROM THE DATA ITSELF IS SUPER
IMPORTANT AND WE'VE DEVELOPED A
FEW WAYS OF DOING THAT.
YOU HAVE TO ALSO THINK ABOUT THE
THRESHOLD THE ENTRY THRESHOLD OF
ANY OF THESE MECHANISMS.
ONE THING THAT WE PUT TOGETHER
WAS A USER COMMENT SYSTEM WHICH
ALLOWS ANYBODY TO LOG IN AND PUT
A COMMENT ON A GENE PAGE.
AND THE COOL THING IS THAT NOW
WE HAVE PEOPLE ON A REGULAR
BASES WHO GO IN AND ENTER
COMMENTS ON GENES.
EITHER SOMETHING PUBLISHED OR
SOMETHING THAT THEY HAVE
DISCOVERED THEMSELVES IN THE LAB
BUT NEVER PUBLISHED.
OR IT'S NOT PUBLISHABLE
INFORMATION BUT STILL USEFUL
INFORMATION AND WE HAVE OVER
40,000 USER COMMENTS IN THE
DATABASE.
SO THERE ARE 40,000 COMMENTS
FROM INDIVIDUALS WHO OVER THE
YEARS WHO HAVE ENTERED COMMENTS.
AND MANY OF THEM ARE VERY USEFUL
WHICH TELLS YOU SOMETHING ABOUT
A GENE THAT WAS NOT KNOWN
BEFORE.
WE HAVE SOME MORE SOPHISTICATED
WAYS.
FOR EXAMPLE -- NOT ONLY THE
GENOMES THAT WE HAVE IN OUR
DATABASES -- GENE STRUCTURE AND
ALTERNATIVE PLEASE VARIANTS.
WE HAVE INTEGRATED A TOOL CALLED
APOLLO THAT ALLOWS THE USESSER
TO GO IN AND STRUCTURE A GENE
MODEL AND CONTRIBUTE IT BACK AND
NOW WE MAKE THAT BACK THAT IS
AVAILABLE FOR ANYBODY TO QUERY
AND ANALYZE.
THOSE ARE A COUPLE OF METHODS OF
HOW WE PROVIDE WAYS OF CAPTURING
COMMUNITY KNOWLEDGE.
WE'RE WORKING ON EXCITING WAYS
THAT WILL ALLOW USERS TO UPLOAD
AND ANALYZE THEIR OWN DATA.
AND THEN TO ALLOW USERS TO FLIP
THE SWITCH AND MAKE IT AVAILABLE
TO THE COMMUNITY.
ANOTHER WAY TO CAPTURE USER
ANALYSIS THAT CAN BE INTEGRATED
SO PEOPLE CAN RUN EXPERIMENTS
USING INTEGRATED DATA.
>>MICHAEL, DID YOU WANT TO ADD
ANYTHING FROM THIS ASPECT FROM
YOUR STUDIES.
>>WELL HE COVERS IT PRETTY
WELL.
JUST, TO ME I'M VERY INTERESTED
IN THE IDEA THAT THE CAPTURING
OF KNOWLEDGE AND WHAT WE KNOW.
AND AN ATTEMPT TO -- BECAUSE THE
TREMENDOUS INEFFICIENCY IN THE
SCIENTIFIC PROCESS.
IT'S REALLY VERY, VERY
INEFFICIENT.
7 AND I THINK THAT IF
INFORMATION AND KNOWLEDGE WERE
STRUCTURED BETTER, YOU CAN'T BE
MAYBE KIND OF REALLY TOO
AMBITIOUS BUT YOU CAN KNOWLEDGE
PICK A DOMAIN OF KNOWLEDGE THAT
YOU WORK IN AND IF THERE WAS A
CENTRALIZED STATE OF WHAT WAS
NONAND WHAT DATA CONTRIBUTED TO
THAT AND IF THERE WAS SOMETHING
THAT IS NEW THAT YOU CAN MAP IT.
THE EFFICIENCY TO MOVE FORWARD
WOULD BE SO MUCH GREATER.
I DON'T KNOW WHEN WE'LL GET
THERE.
BUT YEAH.
THAT IS THE ASPIRATION.
>>THAT IS DEFINITELY THE
ASPIRATION.
AND AS WE ALL KNOW IS THAT
SCIENCE IS NOT STATIC.
SO SIMILAR TO THE QUESTION TO
THE LAST PANEL WHAT ARE THE
PROSPECTS FOR USING AI TO
FACILITATE QUERIES IDENTIFYING
GENES AND CELLS OF INTEREST.
MICHAEL, THAT IS A QUESTION FOR
YOU
>>I DON'T KNOW.
WE THINK OF AI AS DIFFERENT
THINGS.
RECAPITULATION OF STUFF THAT WE
KNOW ALREADY AND THE
PRESENTATION OF IT AND POSSIBLY
CONNECTION OF INSIGHTS WHICH ARE
ACTUALLY REFERRING TO CONNECTING
THINGS IN DATABASE STRUCTURE
THAT WE UNDERSTAND.
BUT THERE IS ANOTHER LEVEL THAT
WE APPLY IT BEFORE THAT WHICH IS
TYPICALLY ADVANCED MACHINE
LEARNING TO TEASE OUT
SIGNIFICANCE IN DATA AND
ASSOCIATIONS AND THAT STILL HAS
A LONG WAY TO GO.
FOR EXAMPLE, JUST UNDERSTANDING
GENE REGULATORY MECHANISMS AND
THE MAPPING OF THE EPIGENETIC
DATA AND ITS SIGNIFICANCE IS A
MAJOR INITIATIVE.
AND I THINK THAT I PREFER TO SEE
THE APPLICATION OF THESE MACHINE
LEARNING TECHNOLOGIES THERE IN
TERMS OF TRYING TO REALLY DEEPLY
TEASE OUT WHAT WE CAN LEARN
RATHER THAN APPLYING THEM AT AN
ORGANIZATIONAL RECAPITULATION
LEVEL.
THERE IS STILL SO MUCH WORK TO
DO THERE.
I THINK WE'RE ALWAYS DAUNTED BY
THE COME -- COMPLEXITY OF THE S.
WE DON'T REALLY UNDERSTAND
C-ELEGANS.
AND THAT SAYS SOMETHING ABOUT
OUR ABILITY TO UNDERSTAND THE
HUMAN BRAIN.
>>WELL PUT.
OMAR.
>>DEFINITELY.
I SHOWED THAT SEARCH STRATEGY.
THAT CHOO-CHOO TRAIN THAT IS
INTEGRATED TOGETHER.
WE VIEW THAT AS A POOR PERSON'S
CHAT GPT.
WHAT I'VE DONE WITH ONE OF THESE
STRATEGIES IS THAT THEY ASK
MULTIPLE QUESTIONS AND PROVIDING
THE STRUCTURE TO IT.
WHAT WE WOULD LIKE TO DO AND
THAT IS OBVIOUSLY PART OF OUR
NEXT ITERATION.
TO USE NATURAL LANGUAGE SOFTWARE
TO COME IN AND SAY WELL I WANT
TO FIND ALL OF THE GENES THAT
MIGHT BE EXAMPLE PRESSESSED IN
THE -- STAGES -- AND BY THE WAY
MAKE SURE THAT THEY ALSO HAVE
EVIDENCE FROM AT LEAST FIVE
STUDIES.
7 BASICALLY WE CAN CODIFY THAT
STRATEGY INTO INQUIRIES AND AS
LONG AS WE CAN LINK THAT BETWEEN
NATIONAL LANGUAGE DECIPHERING
MECHANISMS THEN WE MAY BE IN A
PLACE FOR USERS TO TYPE A
QUESTION LIKE THAT.
THERE IS MORE DATA THAT WE HAVE
AND WE CAN ALSO START APPLYING
MACHINE LEARNING METHODS TO FIND
CONNECTIONS AND THREADS SO WE
HAVE VARIOUS TYPES OF DATA TYPES
BUT WE NEED MORE AND THE MORE
THAT WE GET THE MORE WE CAN FEED
THESE MACHINE LEARNING
ALGORITHMS TO COME UP WITH
INFORMATIVE RESULTS
>>THAT IS A VERY GOOD POINT
BECAUSE THE DIFFERENT DATA THE
DIFFERENCES IS QUITE DIFFERENT.
AND MICHAEL TOUCHED ON THE --
TRANS DOME AND THERE ARE SO MANY
THINGS THAT WE NEED TO EMBARK
ON.
BUT I'M GLAD THAT YOU'RE BOTH
THINKING ABOUT THAT.
THERE WAS A QUESTION.
IS THERE SUPPORT FOR LINKING THE
GENOMIC DATA TO RELATED CLINICAL
DATA.
THAT IS A LOADED QUESTION.
>>NOT IN OUR CASE YET.
EXCEPT AS I INDICATED THROUGH
MAPPING THROUGH THE SAME
TAXONOMY AND ITS ORGANIZATION.
FOR EXAMPLE OUR ALZHEIMER'S
DATABASE WHICH YOU CAN LINK AND
MAP BACK TO THE SAME CELL TYPES.
IN THAT SENSE YES.
BUT IN THE GENERAL SENSE OF
LINKING OUT TO LARGER COMMUNITY
DATASETS IN ALZHEIMER'S DISEASE
NO.
NOT YET.
THE DATA IS AVAILABLE.
PEOPLE CAN DO IT.
BUT IT'S NOT IN A USER-FRIENDLY
WAY.
>>SO WE HAVE MECHANISM IN PLACE
WHERE WE CAN LOAD CLINICAL
INFORMATION.
SO METADATA -- ABOUT THE
INDIVIDUALS THAT THE SAMPLES
CAME FROM AND FOR EXAMPLE IF YOU
HAVE INDIVIDUALS WHOSE MALARIA
ISOLETTES WERE SEQUENCED WE HAVE
WAYS OF CAPTURING THAT AND
MAKING THOSE INQUIRIES.
AN IMPORTANT POINT.
THERE ARE A LOT OF LARGE STUDIES
OUT THERE THAT ARE RICH WITH
DATA. THEY INCLUDE A LOT OF
DATA AND YOU WANT TO BE ABLE TO
CORRELATE AND INTEGRATE AND THEN
LOOK AT THE GENOME SEQUENCES OR
REQUEST SAMPLES.
AND I WOULD LIKE TO GIVE A SHOUT
OUT TO ANOTHER PROJECT THAT PART
OF OUR GROUP THAT WORKS ON THE
BRC FUNDED, IT'S CALLED KLIN --
F PB.
TO MAKE THE DATA AVAILABLE IN A
WAY WHERE EVERYBODY CAN CARY IT
AND ACCESS IT.
THAT IS A PRETTY COOL STUDY.
A HUGE AMOUNT OF POTENTIAL.
BUT WE NEED SOME VISIONARY
FUNDERS TO SAY THAT THIS
REQUIRES SOME TEETH BEHIND IT TO
MAKE SURE THAT IT'S SUSTAINABLE
AND WORKS.
THAT IS EXCITING AND I HOPE THAT
WILL PROGRESS.
>>THAT IS A GOOD POINT IN TERMS
OF THE FUNDERS AND THERE WAS A
QUESTION EARLIER ARE THERE
SOCIETIES OR ORGANIZED GROUPS
HELPING TO PROVIDE AWARENESS OF
YOUR RESOURCES AND IN TERMS OF
FURTHER DEVELOPMENT AND
EXPANSION.
>>I CAN TELL YOU FROM MY
PERSPECTIVE BECAUSE WE'VE BEEN
WORKING HANDLED IN HAND WITH THE
COMMUNITY FOR YEARS.
IT'S VERY EXCITING TO ATTEND
CONFERENCES BECAUSE WE OFTEN GET
INVITED TO COME AND MANY THAT WE
ATTEND WE'RE REQUESTED TO HAVE A
BOOTH.
AND PROVIDE SUPPORT TO OUR
USERS.
AND IN MOST CASES WE DON'T EVEN
HAVE TO PAY FOR THE BOOTH.
THIS IS A COMMUNITY RESOURCE AND
WE WANT YOU TO BE THERE.
YOU'RE SUPPORTING THE COMMUNITY
AND THAT IS THE WAY IT SHOULD BE
FOR COMMUNITIES THAT IT'S
BENEFITING.
THEY ARE EXCITED ABOUT HAVING
US.
OF COURSE, THAT DOES NOT
NECESSARILY TRANSLATE INTO
FUNDING US BUT THEY MAKE SURE
THAT WE'RE ENGAGED WITH THE
COMMUNITY.
>>WELL I THINK FROM THIS
PERSPECTIVE THIS PRIZE THIS
AWARD IS REALLY IMPORTANT.
SO I HAVE TO ASK THE QUESTION.
HOW DID YOU EACH FIND OUT ABOUT
THE DATA WORKS CHALLENGE?
OMAR, DO YOU WANT TO GO FIRST?
>>SURE.
WE ACTUALLY WERE MADE AWARE BY
THE CHALLENGE FROM OUR
CONTRACTING OFFICER, MARIA --
SHE IS AT NIAID.
THIS IS GOING ON IN CASE YOU MAY
BE INTERESTED.
IN THE EARLY DAYS WHEN IT WAS
FIRST ANNOUNCE ENDS -- ANNOUNC.
EVERYTHING THAT WE'RE DOING FITS
THE REQUIREMENT AND WE THOUGHT
WE HAD A GOOD CHANCE AND SUPER
HAPPY THAT WE'RE AMONG THE
PEOPLE THAT ARE RECOGNIZED.
>>A GREAT RECOGNITION.
DEFINITELY.
MICHAEL.
>>I THINK I JUST DISCOVERED IT
THROUGH SOME OF YOUR PUBLICITY
AND ADVERTISING.
AND I CAME UPON IT AND I WAS
REALLY SO IMPRESSED WITH THE
IDEA THAT PEOPLE INVOLVED IN
THIS ACTIVITY WOULD POTENTIALLY
BE RECOGNIZED IN THIS WAY THAT
IT WAS SO NOVEL THAT I THOUGHT
WOW, THIS IS JUST GREAT.
AND I THOUGHT ABOUT THE ALLEN
INSTITUTE OF PU PUTTING OUT DATA
AND MAYBE WE WOULD STAND A
CHANCE OF DOING SOMETHING THAT
COULD BE RECOGNIZED THERE.
SO THAT IS WHY I ORGANIZED OUR
TEAM AND WE PUT IT FORWARD.
WE'RE VERY GRATEFUL CERTAINLY TO
THE NIH AND FASEB FOR
ESTABLISHING THIS.
THIS IS A PRESTIGIOUS AND
REMARKABLE THING
>>CONGRATULATIONS TO BOTH OF
YOU AND YOUR TEAMS.
IT IS TRULY BOTH WONDERFUL
EXAMPLES ABOUT THE POWER OF TEAM
SCIENCE AND COLLABORATION AND
DATA SHARING.
WHAT WE'RE ALL ABOUT HERE.
SO THANK YOU TO FASEB AND THANK
YOU TO NIH BECAUSE IT SOUNDS
LIKE THERE WILL BE ADDITIONAL
FUNDING TO CONTINUE THIS.
WE WANT TO HEAR ABOUT HOW DATA
SHARING WORKS AND HOW IT CAN
HELP ADVANCE OTHER RESEARCH.
I THINK THAT IS IT FOR OUR
QUESTIONS.
AND WHAT I WOULD LIKE TO DO IS
TELL EVERYONE THAT RIGHT NOW WE
ARE GOING INTO A 15 MINUTE
BREAK.
BUT WE WILL CONVENE AGAIN AT
1:15 WITH TWO ADDITIONAL PANELS
AND I LOOK FORWARD TO SEEING YOU
AGAIN AND OMAR AND MICHAEL THANK
YOU VERY MUCH FOR YOUR
PRESENTATIONS.
THIS WAS TERRIFIC.
>>THANK YOU.
>>ALL RIGHT.
SEE EVERYONE AT 1:15 AFTER THE
BREAK.
>>WELCOME
BACK EVERYBODY TO THE DATA WORK
PRIZE SYMPOSIUM.
WE'LL HEAR FROM THREE * REALLY
COOL PROJECTS.
WE'LL HAVE MONICA MUNOZ-TORRES
WHO WILL TALK ABOUT THE
INITIATIVE.
NEXT WE'LL HAVE THE ANALYSIS
GROUP AND FINALLY TO CLOSE OUT
THE PRESENTATION PART WE'LL HEAR
FROM GIULIO FORMENTI.
AFTER THE PRESENTATION WE'LL
HAVE A Q&A SESSION AND WE INVITE
YOU TO SUBMIT YOUR QUESTIONS ON
SLIDO.
EVENT NUMBER 1379.
MONICA, PLEASE GO AHEAD.
>>THANK YOU.
DO YOU HAVE MY SLIDES?
>>YES.
>>WONDERFUL.
IT'S MY PLEASURE TO BE HERE
TODAY.
I'M MONICA MUNOZ-TORRES AND THE
PROGRAM DIRECTOR.
TODAY I'M GOING TO TAKE TIME TO
BRIEFLY SHARE ABOUT WHAT AND WHO
IS THE MONARCH INITIATIVE AND
SHARE DETAILS ABOUT OUR TEAM AT
THE END OF THE PRESENTATION.
THE MONARCH INITIATIVE -- IT WAS
LAUNCHED IN 2013 AND OUR INITIAL
GOAL WAS TO LEVERAGE ALL OF THE
DATA FROM ACROSS ORGANISMS AND
SOURCES TO IMPROVE RARE DISEASE
DIAGNDIAGNOSTICS.
MONARCH RESOURCES ARE NOW
INTEGRATED AND WE WORK CLOSELY
WITH A VARIETY OF COMMUNITIES TO
HELP CENTRALIZE DATA FOR REUSE.
WE BELIEVE THAT IT'S IMPORTANT
TO MAXIMIZE THE UTILITY AND
IMPACT.
IT IS COMPOSED OF MULTIPLE OPEN
SOURCE AND OPEN ACCESS.
WE PROMOTE TRANSPARENT AND NEW
TECHNOLOGIES AND IMPROVE DATA
ACCESSIBILITY.
ALL OF THIS THROUGH THE LIFE
CYCLE OF THE DATA TO MAKE THINGS
OPEN, FAIR AND TRUST WORTHY.
BEFORE I DIG INTO THE DETAILS
I'D LIKE TO SHARE SOME THOUGHTS.
WE BELIEVE THAT IT'S IMPERATIVE
TO CONNECT RESOURCES TO MAKE
THEM COMPUTATIONALLY
DISCOVERABLE.
IN ORDER TO GET THIS BIG PICTURE
IN ORDER TO DETECT GAPS AND
ALLOW FOR BOTH HUMANS AND
MACHINES -- IT IS IMPORTANT TO
CONDUCT CAREFUL ALIGNMENT.
THE INTEREST GRATED DATA PAINTS
A MORE CA -- A MORE COMPREHENSIE
PICTURE.
WE UNIFY AND HARNESS THIS WIDE
RANGE OF DATA USING DATA
INTEGRATION.
WE INCREASE UTILITY FOR
RESEARCHERS ACROSS DISEASES AND
ACROSS SPECIES.
AND WE ALSO DO THIS BY
GALVANIZING.
ACROSS A WIDE RANGE OF
ORGANISMS.
AND BY MAXIMIZING DATA REUSE
FROM A DESPERATE LANDSCAPE OF
DISEASE WE BELIEVE THAT WE'RE
FILLING THIS CRITICAL GAP IN THE
AVAILABILITY OF THE GENOTYPE
DATA.
WE'VE BEEN WORKING ON CREATING
TOOLS TO HARMONIZE DATA FOR
COMPUTATIONAL ISSUE.
WE UNDERSTAND THE LACK OF
CONSISTENCY CURATES DIFFERENT
ASSOCIATIONS SUCH AS THE DISEASE
VARIENT.
IT IS VERY CHALLENGING.
WE INCLUDE FINDING BEST
PRACTICES AND COMMUNITY
DEVELOPMENT OF SPECIES AND
ONTOLOGIES.
MAN O DAND THE WIDE RANGING IMPF
MONARCH RESOURCES WE BELIEVE IS
UNDERSTATED.
STANDARDIZING THE ENTIRETY OF
ANATOMY.
SUPPORTING OTHER RESOURCES AND
THEIR EFFORTS.
TO BE INNER OPERABLE.
WE ACT AS COMMISSIONS AND
RESEARCHERS.
I'LL DIG MORE IN TO THIS IN HOW
WE IMPACT THE COMMUNITY.
THE INTEGRATION ASPECT.
BY MAKING DATA MORE INNER
OPERABLE WE HELP SUPPORT A WIDE
RANGE OF DATA SHARING AND REUSE.
AND THE MONDO DISEASE ONTOLOGY
-- SO COMBINED -- INTO AN
ONTOLOGY THAT IS COHERENT.
AND IT HAS ALSO BEEN A
MULTIPLIER EFFECT BECAUSE IT
BRINGS EXPERTS AND STAKEHOLDERS
THAT CAN WORK TOGETHER TO FIGURE
OUT HOW TO BEST CATEGORIZE
DISEASE.
THIS IS ANOTHER EXAMPLE OF
INTEGRATION WHERE WE SHARE
INFORMATION THAT HAS BEEN AND IT
HAS BEEN DESIGNATED AS A
STANDARD.
LASTLY ANOTHER EXAMPLE OF
INTEGRATION HAS BEEN THE HBO
WHICH IS MADE MORE ACCESSIBLE BY
BEING TRANSLATED INTO 20
DIFFERENT LANGUAGES.
SOME OF THE STANDARDS INCLUDE
THE LINK ML BASED KNOWLEDGE --
BIOLINK MODEL.
THIS IS A PIPELINE THAT SUPPORTS
-- THE HETEROGENOUS LANDSCAPE.
AND THEM OUR STANDARD FOR
SHARING PHENOTYPIC DATA.
AND MANY
ONTOLOGIES THAT ARE WIDELY USED
STANDARDS IN AT PHENOTYPE AND
ONTOLOGY.
AND THE ENVIRONMENTAL EXPOSURE
ONTOLOGY.
THE UNIFIED ANATOMY AND THE
PHENOTYPE ONTOLOGY.
SO OUR GOAL IS TO DOUBLE THE
TRANSLATIONAL RESEARCH.
WE TRY TO RECOMMEND HEALTH
CHALLENGES.
THERE IS MORE IN THE LINKS.
BUT WE KNOW THAT IT'S CHAT
CHALLENGING * SO WE NEED MORE
DATA TO MAKE BETTER DIAGNOSIS
AND WE'VE BEEN TRYING TO HELP
IMPROVE THE SITUATION.
NOWADAYS ALMOST ALL TOOLS USE
THIS AND WE'VE DEMONSTRATED THIS
IN A NUMBER OF SETTINGS WITH THE
PROJECT THAT SEQUENCES GENOMES.
SO THE DIAGNOSTIC PIPELINE --
AND THIS IDENTIFIES PATIENTS AND
IN THIS PARTICULAR PAPER FOUND
88% OF RARE DISEASES.
LASTLY I WOULD LIKE TO TALK
ABOUT OUR EFFORTS TO BRING THE
COMMUNITY TOGETHER AROUND THE
RESOURCE.
WE BELIEVE THAT MONARCH SERVES
IS A SOCIAL TECHNICAL MODEL FOR
THE CREATION AND FIT FOR PERFECT
STANDARDS.
LIBRARIES REQUIRE YEARS OF
DEVELOPMENT BUT YEARS OF
COMMUNITY BUILDING AND THESE
RESOURCES ARE USED ACROSS THE
BIODATA RESOURCE ECOSYSTEM
INCLUDING SOME LARGE OPEN
SCIENCE EFFORTS THAT WERE FUNDED
BY THE NIH.
AND THESE PROJECTS WOULD ALSO
HAVE TO SOCIALIZE THEIR OWN
STANDARDS.
IN RESOURCES -- SUCH AS MUNDO
AND HBO.
WE HOPE TO DEMOCRATIZE.
I WOULD LIKE TO SAY GOOD-BYE BY
INTRODUCING THE TEAM.
TO THE TEAM BEHIND ALL OF THIS
WORK AND AS YOU MAY HAVE CAUGHT
ON THE TEAM UNDERLYING MONARCH'S
SUCCESS IS OUR TEAMWORK.
WE HAVE AN INTERNATIONAL TEAM
THAT COMES TOGETHER WEEK AFTER
WEEK TO HELP MOVE FORWARD THESE
RESOURCES.
AND I HAVE THE PRIVILEGE OF
BEING THE PROGRAM DIRECTOR.
WE HAVE THE NAMES OF THE TEAM.
AND I WOULD LIKE TO THANK ALL OF
YOU.
WE KNOW THAT SOME VIEW MONARCH
AS THE TRUE DATA RESEARCH
PARASITE BUT THROUGH COMMUNITY
PARTNERSHIPS AND THROUGH OUR
HARMONIZATION MANY SOURCES HAVE
IMPROVED THEIR DATA PROVISIONS,
THEIR STANDARDIZATION AND THEIR
LICENSING AND THEIR COMMUNITY
ENGAGEMENT.
IT HAS HELPED THE COLLABORATION.
WE'RE EXCITED TO BE HERE TODAY
AND TALKING.
AND I THANK YOU FOR YOUR TIME.
>>THANK YOU SO MUCH.
MONICA FOR THIS GREAT OVERVIEW.
AND NOW WE HAVE NICOLAE DONE.
IF YOU WANT TO GO AHEAD AND TALK
ABOUT THE FORUM.
I DON'T SEE YOUR SLIDES YET.
VERY GOOD.
BUT WE DON'T HEAR YOU OR SEE YOU
IF YOU'RE TALKING.
>>CAN YOU HEAR ME?
>>A BIT FAINT.
>>CAN YOU SEE ME NOW?
>>YEP.
WE CAN SEE AND HEAR YOU AND SEE
YOUR SLIDES.
>>GREAT.
THANKS SO MUCH.
HELLO, EVERYONE.
MY NAME IS NICOLAE DONE.
I'M A MANAGER AT ANALYSIS GROUP
AND I'M DELIGHTED TO SHARE WITH
YOU TODAY OUR WORK ON
COLLABORATIVE DATA SHARE AND
REUSE IN THE RARE DISEASE SPACE.
I'M ALSO REPRESENTING A LARGE
INTERDISCIPLINARY TEAM OF
RESEARCHERS AND PATIENT
ADVOCATES.
OUR WORK WAS FUNDED BY THE
RESEARCH CONSORTIUM.
A NONPROFIT ORGANIZATION THAT
AIMS TO ACCELERATE AND ALSO GIVE
ACCESS TO NEW TREATMENTS AND TO
DRIVE ADVANCES IN THE AWARENESS
OF SCIENCE IN THE TREATMENT OF
THESE DISEASES.
THIS RESEARCH IS ALSO CONDUCTED
UNDER THE EMILY DOZEN FORUM.
* AND THE STUDY INVOLVES THE
VIOLATION OF A CARDIAC
BIOMARKER.
AND JUST TO GIVE YOU MORE OF A
BACKGROUND ABOUT THIS DISEASE.
THIS IS A RARE AND ALSO
PROGRESSIVE OFTEN FATE A DISEASE
CAUSED BY THE ACCUMULATION OF
SO-CALLED AMYLOID IN DIFFERENT
ORGANS.
A VARIETY OF SYMPTOMS FROM
DIZZINESS TO FATIGUE TO HEART
AND KIDNEY FAILURE.
A VERY DEBILITATING DISEASE AND
HIGHLY PET OHIO AGAIN US.
-- HETEROGENEOUS.
*.
CARDIAC INVOLVEMENT IS THE MOST
COMMON AND THE EXTENT TO WHICH
CARDIAC INVOLVEMENT IS PRESENT
TENDS TO BE A VERY GOOD
PREDICTOR OF MORTALITY IN
PARTICULAR.
SO JUST TO ILLUSTRATE MEDIUM
SURVIVAL TIME FOR PATIENTS WITH
SEVERE CARDIAC INVOLVEMENT IS
LESS THAN ONE YEAR.
WHERE FOR THOSE WITHOUT CARDIAC
INVOLVEMENT IS 8 YEARS.
A HUGE DIFFERENCE.
ABOUT ONE THIRD OF EARLY DEATHS
WITHIN 90 DAYS OF DIAGNOSIS ARE
ATTRIBUTED TO CARDIAC
INVOLVEMENT.
SO CURRENTLY THERE ARE VERY FEW
TREATMENTS THAT ARE EFFECTIVE.
THERE WAS ONE TREATMENT APPROVED
IN 2021 BUT IN GENERAL THERE ARE
NOT MANY OPTIONS.
AND THIS SITUATION REFLECTS THE
FACT THAT DRUG DEVELOPMENT IS
EXTREMELY DIFFICULT IN THIS
SPACE FOR REASONS THAT ARE
COMMON TO OTHER RARE DISEASES.
RELATED TO THE AUDIENCE
INCLUDING PROHIBIT -- COSTLY AND
LENGTHY CLINICAL TRIALS.
AND IN ADDITION TO THE
DIFFICULTIES OF CONDUCTING
CLINICAL TRIALS IN THE FIRST
PLACE THERE ARE MANY BARRIERS
THAT PREVENT OR LIMIT THE
SHARING OF DATA.
WHICH TEND TO END UP SILOED AT
MEDICAL INSTITUTIONS AND MEDICAL
CENTERS.
SO HERE I'M ONLY MENTIONING A
FEW OF THE MOST IMPORTANT
BARRIERS.
BUT IT'S ALSO WORTH NOTING THAT
THESE BARRIERS OFTEN STEM FROM
VERY LEGITIMATE UNDERSTANDABLE
CONCERNS OF PROTECTION OF
PATIENT'S PRIVACY OR CONCERNS
THAT THE DATA WILL BE MISUSED OR
MISINTERPRETED.
SO SIMPLY FROM VARIOUS
LIMITATIONS AND THE TECHNICAL
AND FINANCIAL INSTITUTIONAL
CAPACITY TO SHARE THE DATA.
SO I JUST WANT TO EMPHASIZE THE
POINT THAT MAY BE OBVIOUS TO YOU
BUT IT'S STILL IMPORTANT TO
HIGHLIGHT IS THAT DATA IS NOT
RANDOM BYTES FLOATING THROUGH.
FREEING IT UP FOR REUSE AND
HARNESSING IT FOR SCIENTIFIC
RESEARCH NEEDS TO BE DONE
THOUGHTFULLY AND RESPONSIBLY.
THE IDENTIFICATION OF SURROGATE
END POINTS WHICH ARE MEASURES
THAT ARE NOT THEMSELVES DIRECT
MEASUREMENTS OF CLINICAL BENEFIT
BUT ARE KNOWN TO PREDICT
CLINICAL BENEFIT AND CAN BE USED
TO DEMONSTRATE IT EITHER ALONG
AN ACCELERATED OR TRADITIONAL
APPROVAL PATHWAY IN THE
REGULATORY FRAMEWORK.
SO IN PARTICULAR THERE IS ONE
BIOMARKER CALLED THE -- VERY
LONG -- A MOUTHFUL OR NTOB FOR
SHORT.
IT'S ESSENTIALLY A SIGNALING A
MOLECULE THAT WAS LEVEL IN
INCREASES IN BLOOD WHEN THE
HEART MUSCLE IS UNDER STRESS AND
ALSO A USEFUL BIOMARKER IN HEART
FAILURE FOR EXAMPLE.
HOWEVER I WOULD NOTE THAT THE
UNDERLYING MECHANISM IS RELEASED
IN HIGHER QUANTITIES AND IT IS
DIFFERENT AT A PHYSIOLOGICAL
LEVEL.
WITHOUT GOING INTO ALL OF THE
CLINICAL DETAILS HERE SUFFICE TO
SAY THAT THE MECHANISM
UNDERLYING THE CHANGES IS QUITE
WELL UNDERSTOOD BY CLINICIANS
AND RESEARCHERS.
SCENES,LY THE BIOMARKERS SIGNAL
THAT THE CELLS ARE GETTING
SQUISHED AND UNDER INCREASED
STRESS AND THIS BIOMARKER IS
WELL UNDERSTOOD CLINICALLY.
BUT FROM A STATISTICAL POINT OF
VIEW THERE ARE THREE LEVELS OF
EVIDENCE THAT NEED TO BE
SATISFIED TO BE CONSIDERED A
VALIDATED SURROGATE FOR
SURVIVAL.
SO FIRST WE NEED TO KNOW THAT
THE LEVELS OF THE BIOMARKERS ARE
ASSOCIATED WITH SUBSEQUENT
SURVIVAL.
THE CHANGES IN THE BIOMARKER
LEVEL SHOULD BE PREDICTIVE OF
SURVIVAL AND AT THE THIRD LEVEL
THE DATA FROM MULTIPLE
INDEPENDENT STUDIES ARE NEEDED
TO DEMONSTRATE THE PREDICTIVE
ASSOCIATION.
SO FILES ARE NOW COMPLETED IN
DIFFERENT GEOGRAPHIC LOCATIONS
AND MANY MORE WILL COME ON-LINE
SOON.
AND WE HOPE TO INCORPORATE MANY
IN THE FUTURE BUT FOR NOW WE'RE
ABLE TO USE THE DATA FROM FOUR
TRIALS TWO CONDUCTED IN THE U.S.
AND ONE IN THE EUROPEAN UNION
AND ONE IN CHINA.
OUR OVER-ALL APPROACH TO MAKE
USE OF THIS WITHOUT BEING ABLE
TO ANALYZE IT GIVEN THAT IT IS
NOT SHAREABLE BY THE ACADEMIC
CENTERS THAT CONDUCTED THE
TRIALS IS TO USE FEDERATED
ANALYTICS.
SO USING WHAT IS CALLED A HUB
AND SPOKE PLATFORM WITH THE HUB
INCLUDING A CENTER, A RESEARCH
TEAM FROM ARC AND ANALYSIS
GROUP.
AND THIS TEAM THE CENTRAL TEAM
RESPONSIBLE FOR PUTTING TOGETHER
A COMMON DATA MODEL AND
STATISTICAL ANALYSIS PLAN AND
PROGRAMMING ALL OF THE ANALYSIS
WHICH ARE THEN SHARED SECURING
WITH RESEARCHERS AT EACH OF THE
SPOKE INSTITUTIONS OR THE KIND
OF DIFFERENT CENTERS WHO WOULD
THEN TRANSFORM THE DATA
ACCORDING THE CDM AND REVIEW THE
OUTPUT AND SHARE AGGRAVATED AND
ANALYTICAL RESULTS WITH THE HUB.
I WANT TO SHOW THE NUMBER OF
PATIENTS THAT WE WERE ABLE TO
INCLUDE IN THE ANALYSIS.
SO AFTER ALL OF THE INCLUSION
AND EXCLUSION CRITERIA WERE
APPLIED MOST OF THE TRIALS BEING
PRETTY SMALL WE WERE ABLE TO
INCLUDE ALMOST 300 PATIENTS IN
THE LANDMARK ANALYSIS.
AND ULTIMATELY WE WERE ABLE TO
CONDUCT A META-ANALYSIS
COMBINING AND REUSING THE DATA
FROM THE TRIALS SO THIS BLOCK
HERE SHOWS THE RESULTS OF THE
MODEL META ANALYZING THE DATA.
WE HAVE SOME SIGNAL SO FROM EACH
INDIVIDUAL STUDY THAT THE
TREATMENT RESPONDER, THE
PATIENTS WHO LEVELS GO DOWN
SIGNIFYING IMPROVEMENT.
AND THIS IS THEN ASSOCIATED WITH
BETTER SURVIVAL OR LOWER RISK OF
SURVIVAL.
BUT EACH INDIVIDUAL STUDY ON ITS
OWN DOESN'T HAVE SUFFICIENT
POWER.
SUFFICIENT SAMPLE SIZE TO
DEMONSTRATE THIS EFFECT.
HOWEVER, BY POOLING THE
ESTIMATES FROM THE STUDY WE WERE
ABLE TO ESTIMATE AN OVER-ALL
INCIDENT RATIO OF .39 WHICH IS
STATISTICALLY SIGNIFICANT AND
SUGGEST A 61% LOWER RISK OF
DEATH AMONG THOSE WHO RESPOND.
MEANING THOSE WHOSE LEVELS OF
THIS BIOMARKER INCREASED BY AT
LEAST 30% OR 300 UNITS BY SIX
MONTHS AFTER THE TREATMENT
INITIATION.
SO THIS GIVES US INCREASED
CONFIDENCE THAT THIS BIOMARKER
CAN BE USED AS A SURROGATE
MEASURE FOR OVER-ALL SURVIVAL.
AND WE PRESENTED THESE RESULTS
LAST YEAR AT THE INTERNATIONAL
SYMPOSIUM AND WE'RE DEVELOPING A
MANUSCRIPT BASED ON THESE
RESULTS AT THE MOMENT AS WELL.
SO IN CONCLUSION I WANTED TO END
WITH JUST SAYING THAT OUR STUDY
DEMONSTRATED SUCCESSFUL DATA
SHARING AND REUSE CASE FOR
COLLABORATIVE LEARNING IN THE
AREA DISEASE SPACE.
AND SPECIFICALLY IN THE SEVERE
AND PROGRESSIVE DISEASE LIKE AM
LEO DOSES.
* AND THE VALIDATION OF THE PART
OF THE BIOMARKER THAT CAN BE
USED AS A SURROGATE END POINT IN
FUTURE STUDIES.
BECAUSE OUR PLATFORM WAS
DESIGNED TO BE MODULAR IT CAN BE
EXTENDED TO INCORPORATE NEW
STUDIES AND OUTCOMES AND ALSO
ANALYSIS.
AND WE USED OPEN SOURCE
ANALYTICAL TOOLS AND RELIED ON
COMMON DATA MODEL BASED ON DATA
FORMATS AND ALSO MAKE OUR CODE
AND DOCUMENTATION AVAILABLE TO
OTHERS.
SO I'LL STOP HERE AND BEFORE I
DO THAT I'LL ACKNOWLEDGE THE
MANY WONDERFUL FOLKS AND
INSTITUTIONS WHO MADE THIS
PROJECT POSSIBLE INCLUDING THE
INDUSTRY AND RESEARCH GROUPS WHO
SHARED THEIR DATA FOR THE
GREATER BENEFIT OF THE
SCIENTIFIC COMMUNITIES.
THE FORUM FOR PROVIDING THE
SCIENTIFIC EXPERTISE AND
INSTITUTIONAL GUIDANCE.
THE RESEARCH CONSORTIUM WHO
SPONSORED THE PROJECT.
AND MY TEAM AT THE ANALYSIS
GROUP WHO MADE THIS HAPPEN.
AND THANK YOU FOR LISTENING AND
I'M HAPPY TO ANSWER ANY
QUESTIONS AND I HAVE MY E-MAIL
HERE IN CASE ANYONE WANTS TO
E-MAIL QUESTIONS AS WELL.
>>THANK YOU SO MUCH FOR THIS
REALLY GOOD EXPLANATION AND
IMPACTFUL PROJECT.
NOW WE'LL MOVE ON TO GIULIO
FORMENTI.
BEFORE WE DO THAT I REMIND ME WE
HAVE SLIDO FOR QUESTIONS.
PLEASE POST YOUR QUESTIONS
THERE.
AGAIN THE NUMBER IS 1379.
GO AHEAD.
THANK YOU SO MUCH.
>>THANK YOU SO MUCH FOR THE
INVITATION.
I'M A RESEARCHER AND PROFESSOR
AT THE ROCKEFELLER UNIVERSITY.
SO TODAY I'M PRESENTING ABOUT
THE GENOME ARC.
I WILL BRIEFLY EXPLAIN.
REFERENCE GENOMES -- IT'S
SOMETHING FOUNDATIONAL FOR
BASICALLY MOST GENETIC STUDIES.
WE CAN DEFINE THEM AS ACCURATE
SEQUENCES REPRESENTING THEIR OWN
SPECIES.
AND OF COURSE THIS UNLOCKS MANY
STUDIES IN TERMS OF POPULATION
GENOMICS, GENOME WIDE
ASSOCIATION GENOMICS.
INCLUDING STRUCTURAL FEATURES,
MOBILE ELEMENT AND SO FORTH.
SO WHAT DO WE DO AND HOW DO YOU
RECONSTRUCT?
THE PROCESS IS AN A ASSEMBLY.
SO WE START WITH THE DNA
MOLECULES.
SO WE TAKE THE DNA.
IT HAS TO BE FERMENTED OR
PARTIALLY FERMENTED AND THEN WE
SEQUENCE IT.
RECONSTRUCTION OF THE FRAGMENTS
OF THE ORIGINAL SEQUENCE.
AND THEN RECONSTRUCT THE
ORIGINAL SEQUENCE.
THIS IS COMPLICATED BY FEATURES
OF THE GENOMES.
THE MOST CHALLENGING ONE HAS
BEEN REPETITIVE ELEMENTS.
THEY ARE REPRESENTED BY THE
ARROW.
7 AND DOWNSTREAM YOU HAVE THE
SAME SEQUENCE REPEATED.
AND TYPICALLY UNTIL A FEW YEARS
AGO PEOPLE USED SEQUENCES.
THE PROBLEM IS THAT IF YOU USE
SHORT READS -- IT'S CALLED --
SEQUENCING.
AN INSTRUMENT THAT ALLOWS TO YOU
READ MUCH LONGER READS.
FROM 10 TO 100.
SO VERY LONG READS.
YOU'RE SIMPLIFYING YOUR
CONSTRUCTION PROBLEM BY
INTRODUCING THESE LONG READS.
THESE ARE VERY USEFUL AND OF
COURSE A LOT OF PEOPLE AROUND
THE WORLD ARE NOW CONTRIBUTING
TO GENERATING REFERENCE GENES
USING THESE NEW TECHNOLOGIES.
IN OUR CASE WE'RE PART OF THE
NETWORK AND PROJECT CALLED
FURTHER THE GAMES PROJECT.
THERE ARE THREE MAIN HUBS.
THE HUBS WHERE WE GENERATE THE
PRIMARY DATA.
AND THE GOAL IS TO GENERATE THEM
FOR ALL 70,000 INVERTEBRATE
SPECIES.
WE'RE PLANNING TO DO THIS IN
MULTIPLE PHASES.
THE GOAL IS TO REPRESENT EACH
ORDER OF THE VERTEBRATES WITH AT
LEAST ONE REPRESENTATIVE
SPECIES.
AND THEN WE'LL MOVE ON TO FAMILY
AND -- SPECIES.
AND THIS IS A BROAD NETWORK.
WE'RE NOT JUST THREE HUBS BUT
THERE IS HUNDREDS OF
COLLABORATORS ACROSS THE GLOBE
THAT CAN CONTRIBUTE BY PROVIDING
SAMPLES.
BY PROVIDING DATA.
THEY CONTRIBUTE BY PROVIDING
ANALYSIS.
SO THIS NETWORK IS WHAT REALLY
PROMPTED THE CREATION OF THE
GENOMEARK.
WE NEEDED A WAY TO BE ABLE TO
SHARE ACROSS THE NETWORK AND
ALSO WITH OTHER PEOPLE THAT ARE
STAKEHOLDERS.
THE DATA THAT WE WERE
GENERATING.
THIS IS THE TEAM HERE AT
ROCKEFELLER.
WE HAVE PEOPLE FROM THE
SEQUENCING LAB.
WE HAVE PEOPLE FROM THE
BIOINFORMATIC LAB.
SO WHAT IS THE GENOMEARK.
IT'S A POWERED REPOSITORY.
WE STORE EVERYTHING THAT WE
GENERATE IN THERE.
WE STORE THE RAW DATA. WE STORE
THE ASSEMBLIES.
THE FILES, METADATA AND WE
INTEGRATE ALL OF THIS INTO A
SERIES OF PORTALS WHERE THE DATA
BECOMES ACCESSIBLE TO EVERYONE.
THIS IS A PUBLICLY AVAILABLE LAB
SO WE'RE NOT PUTTING ANY EMBA
EMBARGO.
TH-- IT BECOMES IMMEDIATELY
AVAILABLE TO BE STUDIED.
SO THIS GENOMEARK ALLOWS US TO
SHARE DATA ACROSS THE DIFFERENT
SITES AND WITHIN THE NETWORK AND
ALLOWS ANYONE IN THE COMMUNITY
TO VISUALIZE IT.
HERE IS AN EXAMPLE OF ONE OF THE
PORTALS.
YOU CAN SEE WHAT ARE THE READS
THAT ARE AVAILABLE.
FOR A PARTICULAR TECHNOLOGY.
THE SECOND EXAMPLE, WE HAVE
THREE DIFFERENT DATA TYPES
AVAILABLE FOR THIS PARTICULAR
SPECIES.
YOU CAN CLICK ON THE LINK OR USE
THE COMMAND LINE.
IT CAN BE DOWNLOADED.
SO LET'S ZOOM IN TO ONE SPECIFIC
SPECIES.
YOU HAVE THE GENERAL DESCRIPTION
OF THE SPECIES.
A SECTION HERE WHICH IS MOST
IMPORTANT.
WHAT WE GENERATE WE ALSO MAKE
AVAILABLE IN THE PUBLIC
REPOSITORIES THAT ARE NOT
MAINTAINED BY US.
GENE BANK.
THE YOU A -- EUROPEAN ARCHIVE AD
AGAIN WE PUBLISH THE ASSEMBLIES,
THE RAW DATA AND SO FORTH.
BUT AT THE SAME TIME WE PROVIDE
OUR OWN STORAGE SYSTEM.
AND YOU CAN SEE HERE BELOW --
YOU CAN HAVE ALL OF THE DATA
TYPES FOR THE PARTICULAR
SPECIES.
ADDITIONAL TECHNOLOGY THAT IS
USEFUL TO RECONSTRUCT THE GENOME
AND YOU CAN SEE SOME OF THE
STATISTICS AND THE NUMBER OF
BASES.
AND THEN BASICALLY YOU HAVE THE
ASSEMBLIES WHICH YOU CAN SEE
HERE.
AGAIN JUST CLICK ON THE LINK.
YOU WILL BE ABLE TO DOWNLOAD THE
GENOME AND THIS IS IN BATCHES SO
YOU CAN ACCESS ALL OF THE
GENOMES THAT ARE AVAILABLE AT
GENOMEARK.
YOU CAN ALSO HAVE ACCESS TO THE
MITOCHONDRIAL DNA IF INTERESTED.
A SERIES OF PROJECTS THAT ARE
GENERATING REFERENCE FOR
NONMODEL SPECIES.
SO WITH THAT I WOULD LIKE TO
THANK MY COLLABORATORS HERE AT
ROCKEFELLER AND I'M HAPPY TO
ANSWER QUESTIONS WITH THE REST
OF THE SPEAKERS.
THANK YOU VERY MUCH.
7
>>THANK YOU SO MUCH FOR THIS
PRESENTATION.
NOW I INVITE OUR PRESENTERS TO
COME BACK ON THE SCREEN FOR Q&A.
WE ALREADY HAVE A COUPLE OF
QUESTIONS ON OUR CHAT BUT I
INVITE AGAIN PEOPLE TO POST
MORE.
I'M GOING TO START WITH THE
FIRST QUESTION THAT IS TO ALL OF
YOU.
WHAT ARE YOUR METRICS FOR
SUCCESS AND HOW DO YOU ASSESS
WHETHER THESE RESOURCES ARE
ACHIEVING THEIR GOAL?
LET'S GO IN PRESENTATION ORDER.
MONICA, YOU WANT TO START?
>>SURE.
THANK YOU.
THIS IS GREAT.
OUR METRICS OF SUCCESS SHOULD BE
SOMETHING VERY EVIDENT AND OUR
ABILITY TO SEE THAT COMMUNITIES
THAT ARE BOTH UTILIZING THE
RESOURCES AND ALSO GETTING
RESULTS THAT ARE ALLOWING THEM
TO THRIVE.
FOR US THE ABILITY TO SEE REUSE
ACROSS A VARIETY OF RESOURCES
THAT ARE VERY WELL ESTABLISHED
SUCH AS SOME OF THE PROGRAMS AND
NIH AND SOME OF THE PROGRAMS
ACROSS THE WORLD AND THE FACT
THAT WE HAVE MANAGED TO GENERATE
BOTH STANDARDS AND INTEGRATE
RESOURCES THAT ARE BEING USED BY
OTHERS AND LEVERAGED IN ORDER TO
EMPOWER THE DISCOVERY PROCESS.
AND THERE ARE REFERENCES THAT WE
GET IN THE DOCUMENTS ARE SOME OF
THE WAYS THAT WE FEEL CONFIDENT
THAT WE'RE DOING THE RIGHT THING
AND THAT WE'RE ACTUALLY GETTING
TO THE MILESTONES TO BE ABLE TO
SAY THAT OUR RESOURCES ARE BEING
DONE IN AN INWARD MANNER BUT ARE
REFLECTED INTO A WIDER COMMUNITY
COMMUNITY.
AND THE NUMBER OF CONTRIBUTIONS
THAT WE GET BACK INTO OUR
RESOURCES.
WE GO OUT AND DO A LOT OF
OUTREACH TO TRY TO GET -- TO
DISSEMINATE THE RESEARCH TO THE
COMMUNITY AND THEN THE COMMUNITY
BECOMES SO INTERESTED THAT THEY
THEMSELVES WANT TO CONTRIBUTE TO
THE RESOURCES.
SO THE FACT THAT WE'VE BEEN ABLE
TO GATHER A FAITHFUL COMMUNITY
THAT WANTS TO WRITE TICKETS AND
GIVE US INFORMATION AND DO VERY
SPECIFIC TOPIC WORKSHOPS TELLS
US THAT WE'RE DOING SOMETHING
RIGHT.
>>THANK YOU.
WHO WANTS TO GO NEXT?
>>YEAH.
ABSOLUTELY.
I WOULD SAY FOR US AND I
IDENTIFIED TWO TWO AREAS WHERE
WE COULD SEE OR MEASURE SUCCESS.
ONE IS WHERE WE'RE ABLE TO
INCORPORATE ADDITIONAL TRIAL
DATA AND KIND OF WORK WITH
ADDITIONAL COLLABORATORS OR HAVE
THE MODEL -- THAT WE DEVELOPED
AND TO BE USED IN OTHER DISEASE
AREAS AND ANSWER OTHER
SCIENTIFIC QUESTIONS.
THAT WOULD BE ONE VERY IMPORTANT
MEASURE OF SUCCESS.
AND ANOTHER AREA WOULD BE ON THE
REGULATORY SIDE.
IF THE FDA DECIDES TO ALLOW DRUG
DEVELOPERS AND CLINICAL TRIAL
SPONSORS TO MEASURE DRUG
TREATMENT EFFECTS USING A
BIOMARKER TO ACCELERATE THE
INNOVATION AND DRUG DISCOVERY
THEN I WOULD THINK THAT WOULD BE
THE ULTIMATE MEASURE OF SUCCESS
BECAUSE IT REALLY RELATES TO THE
REAL IMPACT THAT IT CAN HAVE ON
PATIENTS.
>>YEP.
SO WE MEASURE OUR SUCCESS IN
DIFFERENT WAYS.
7 OUR PRIMARY GOAL IS TO
GENERATE GENOME OF SPECIES.
WE HAVE SPECIFIC TARGETS.
BUT AT THE SAME TIME IN TERMS OF
DAILY USE WHAT WE LOOK INTO IS
HOW MUCH IMPACT WE HAVE ON THE
SCIENTIFIC COMMUNITY THAT RELIES
ON THE AVAILABILITY OF THESE
GENOMES FOR THEIR RESEARCH.
IF YOU LOOK AT THE NUMBER OF
GENOMES THAT ARE AVAILABLE IN
THE PUBLIC DATABASES IT IS
RELATIVELY SMALL.
THE QUALITY IS HIGHLY VARIABLE
AS WELL.
SO WE ALWAYS MEASURE ON THE
NUMBER OF GENES THAT WE CAN
CONTRIBUTE AND THE IMPACT IN
TERMS OF HOW MANY PUBLICATIONS
USE THIS.
AT THE SAME TIME WE HAVE WANT TO
ELEVATE TO KNOW HOW MANY PEOPLE
USE THIS AND WE TRY TO KEEP
TRACK OF THIS.
AT THE SAME TIME WE HAVE MANY
MORE COLLABORATORS WHO WISH TO
SHARE THEIR DATA WHICH IS AN
IMPORTANT MEASURE OF SUCCESS.
>>SO THERE ARE A COUPLE OF MORE
QUESTIONS IN THE CHAT.
PERTAINING AGAIN TO IMPACT OF
SUCCESS AND SPECIFIC METRICS AND
CHANGES OVER TIME.
SO CAN YOU -- MAYBE CAN YOU
SUMMARIZE WHAT IS THE PLAN TO
COLLECT METRICS AND WHAT METRICS
DO YOU HAVE AND WHAT CHANGES
HAVE YOU SEEN IN THE METHOD
METRICS OVERTIME.
>>I CAN GO.
WE DON'T REALLY HAVE SPECIFIC
METRICS BUT FOR EXAMPLE THERE
ARE CURRENTLY OVER ELEVEN
STUDIES THAT HAVE BEGAN TO READ
OUT THE THE AMOUNT AND WE'RE
PLANNING TO INCORPORATE AS MANY
THOSE AS WE CAN AND KEEPING
TRACK OF ALL OF THE RESEARCH
THAT IS HAPPENING IN THIS SPACE.
SO IDEALLY WE WOULD SEE ALL OF
THOSE TRIALS AND META ANALYZE
THEM INTO THE PLATFORM.
IN TERMS OF THE REGULATORY, MORE
PATIENT CENTERED, I THINK EVEN
ONE THING THAT WE COULD LOOK AT
AND WE DON'T HAVE PLANS TO LOOK
AT THIS -- EVEN IF THERE IS ONE
TRIAL THAT IS SORT OF OR ONE
POTENTIAL DRUG THAT IS ALLOWED
TO BE APPROVED UNDER AN
ACCELERATED PATHWAY BASED ON
DATA AND THEN FURTHER DOWN THE
LINE VALIDATED USING IMPACT ON
MORTALITY OR OTHER METRICS.
I THINK THAT IS SOMETHING THAT
YOU COULD POTENTIALLY MEASURE.
AND ALSO PRESUMABLY THE AVERAGE
LENGTH OF A TRIAL COULD GO DOWN
OVER TIME IN THIS DISEASE SPACE.
DECREASE THE TIME & THE COST.
SO THOSE ARE MEASURABLE.
>>I CAN SHARE SOME FACTS.
I WAS FURIOUSLY LOOKING FOR HARD
NUMBERS TO SHARE BUT DIDN'T
CATCH ANY OF THOSE YET.
BUT I CAN SHARE SOME THOUGHTS.
BECAUSE WE ARE HOPING THAT
PEOPLE WILL REUSE OUR RESOURCES.
WE HAVE GOOGLE ANALYTICS IN
PLACE IN ORDER TO SEE ACCESS TO
THE RESOURCES.
AND TIME THAT PEOPLE REMAIN AND
DOWNLOADS AND ALL OF THESE
THINGS.
WE HAVE STATISTICS FOR APIs.
THERE IS ALSO THE PART IN WHICH
THE COMMUNITY PROVIDES WELL
FIRST WE HAVE THE ABILITY TO
COME TOGETHER.
FOR SPECIFIC TOPIC PANELS.
SO FOR THE HUMAN PHENOTYPE AN TO
THE GEE WE HELD A * WORKSHOP AT
NIH'S OFFICES AND WE WERE ABLE
TO GATHER A COMMUNITY OF ABOUT
30 PEOPLE FROM ACROSS THE
COUNTRY.
AND WE ALL CAME TOGETHER IN
ORDER TO EXPAND THE TERMS FOR
THE PARTICULAR BRANCH OF THE
ONTOLOGY THAT DEALS WITH MENTAL
HEALTH IN TERMS OF BI-POLAR
DISORDER, AUTISM.
WE KNOW NOW THAT WE HAVE BEEN
ABLE TO IMPROVE ON UPWARDS OF
150 TERMS DESCRIBING AUTISM
SPECIFICALLY WITH THE HUMAN
PHENOTYPE ONTOLOGY.
WE'VE BEEN ABLE TO CAPTURE
UPWARDS OF A THOUSAND UPDATES TO
TERMS.
UPDATES TO TEXTURAL.
SO WE KNOW THAT THOSE NUMBERS
ALLOW US TO SEE THE COMMUNITIES
ARE CONSUMING AND PROVIDING
FEEDBACK.
SO WE USUALLY PRESENT TABLES
THAT I DON'T HAVE THAT ALLOW US
TO SEE HOW WELL OUR RESOURCES
ARE BEING ENGAGED WITH AND WHAT
KIND OF FEEDBACK WE RECEIVE FROM
THE COMMUNITY.
WE USE THAT AS A MEASURE OF OUR
SUCCESS.
>>LET ME JUST SAY BRIEFLY THAT
THERE IS SOMETHING CALLED THE
EARTH -- PROJECT THAT COLLECTS
ALL OF THESE PROJECTS.
WE ARE THE SECOND LARGEST
CONTRIBUTOR OF REFERENCE GENOMES
AND THE FIRST ONE FOR -- THAT IS
A MEASURE OF SUCCESS.
>>THANK YOU.
ANOTHER QUESTION FROM SLIDO IS
-- HOW DO YOU LEARN ABOUT THE
PRIZE AND DID THE PRIZE OR
INCENTIVE FOR DATA REUSE SUPPORT
YOUR WORK?
LET'S GO OPPOSITE ORDER.
>>I'LL START.
WELL HONESTLY WE LEARNED ABOUT
THE PRIZE IN A WEEKLY DIGEST
FROM OUR INSTITUTION DEALING
WITH OPPORTUNITIES FOR GRANTS.
I GUESS THAT IS GOOD.
THERE IS A STRONG MOTIVATIONAL
COMPONENT ESPECIALLY WHEN IT
COMES TO MAKE DATA PUBLIC.
AND WE'RE TALKING ABOUT ENTIRELY
NOVEL DATA.
TALKING ABOUT SOMETHING THAT
WE'VE AGAIN SEPARATED WITH OUR
OWN HANDS COLLECTIVELY IN THE
PUBLIC SO MAKING IT ENTIRELY
PUBLIC WAS A VERY GOOD DECISION
IN THE FIRST PLACE.
AND NOT EVERYBODY WAS ON-BOARD
WITH THAT.
BECAUSE WE ARE DOING SCIENCE.
SO THERE IS ALWAYS A RISK'
SHARING AND TWIRLING.
SO RECEIVING THIS KIND OF PRIZE
THAT PROMOTES DATA REUSE AND SO
FORTH IT'S A VERY GOOD INCENTIVE
AND IDEA AND THE SOONER THE DATA
BECOMES PUBLIC AND THE SOONER IT
BECOMES EASY FOR REUSE THE
BETTER.
>>IN TERMS OF HOW WE FOUND OUT
I BELIEVE OUR COLLABORATORS FROM
ARC ARE THE ONES THAT TOLD US
ABOUT THIS PRIZE.
CHALLENGE.
SO I DON'T KNOW HOW THEY FOUND
OUT ABOUT IT.
BUT I'M SURE THEY ARE -- THEY
ARE ON ALL SORTS OF E-MAIL
LISTS.
IN TERMS OF INCENTIVES I WOULD
ECHO WHAT GIULIO SAID,
ESSENTIALLY WE ARE NOW THINKING
MUCH MORE ABOUT HOW CAN WE TURN
OUR FEDERATED ANALYTICS INTO A
PLATFORM THAT CAN BE EASILY USED
AND MORE PUBLIC.
THERE ARE CHALLENGES GIVEN THE
INSTITUTIONAL FRAMEWORK THAT WE
WORK IN.
BUT I THINK IT'S REALLY KIND OF
-- IT HELPED US THINK MORE ABOUT
REALLY SHARING IT MORE BROADLY.
AND MAKING MORE AVAILABLE TO
OTHER RESEARCHERS AND OTHER
INSTITUTIONS.
THAT. MONICA, GO AHEAD.
>>ALL OF OUR RESOURCES DATA,
ONTOLOGIES, STANDARDS, SOFTWARE
TOOLS, ALGORITHMS, WE GENERATE
-- IT'S ALREADY OPEN SOURCE.
AND WE -- I THINK I'M GOING
BACKWARDS.
WHAT HAS INCENTIVIZED FOR US IS
THE CONTINUATION OF BETTER THE
FIND.
WHAT MAKES THINGS FAIR AND
TRUSTWORTHY AND IT'S CHALLENGING
BECAUSE EXECUTING THAT VISION
HAS ITS ROCKS IN THE ROAD.
SO WE CREATED A ROADWAY TO
OPERATE RACIAL EYES * AND ACCESS
AND FINDING AND INTERPRETATION
AND REUSE THERE IS THE NEED TO
TRACE WHERE THE DATA CAME FROM
TO USE THE DATA FREELY WITHOUT
LICENSING.
SO WE WORK REALLY HARD TO GO
BEYOND JUST THE FAIR AND
TRUSTWORTHY TO MAKE SURE THAT
THINGS ARE AVAILABLE TO ALL OF
THE COMMUNITY.
SO, THAT IS KIND OF LIKE WHERE
IT INCENTIVIZES US TO MOVE
FURTHER.
WHERE WE LEARNED ABOUT THIS --
IT'S PROBABLY WE ARE ON A COUPLE
OF DOZEN LISTS FROM NIH AND I
KNOW IT WAS EITHER SOMETHING
THAT CAME FROM THE OFFICE OF
DATA SCIENCE OR PHASE B OR
SOMETHING ALONG THOSE LINES AND
WE JUMPED AT THE OPPORTUNITY TO
BE ABLE TO APPLY
>>THANK YOU SO MUCH.
THANK YOU ALL OF YOU FORGIVING
US A GREAT OVERVIEW OF YOUR WORK
AND NOW WE'LL HAND IT OFF TO
PARKER ANTIN FOR THE NEXT PANEL.
THANK YOU AGAIN.
>>THANKS VERY MUCH.
MY NAME IS PARKER ANTIN.
FROM THE UNIVERSITY OF ARIZONA
AND PAST PRESIDENT OF FAS EB AND
WE HAVE TWO PANELISTS AND THEY
WILL HAVE BRIEF PRESENTATIONS
AND THEN WE'LL MOVE TO PANEL
DISCUSSION.
I WANT TO REMIND YOU THAT IF
YOU'RE NOT ABLE TO ACCESS THE
VIDEO CHAT THE CHAT FUNCTION ON
ZOOM IF YOU'RE ON THE NIH VIDEO
CAST YOU CAN GO IS TO SLIDO .COM
AND ENTER THE NUMBER 1379 TO SEE
THE CHAT THERE AND ENTER ANY
QUESTIONS.
I'M PLEASED TO INTRODUCE OUR TWO
PANELISTS GEORGIO AS SLOWLY AND
EUN-YOUNG MUN.
FIRST UP IS GEORGIO.
A PROFESSOR AT GEORGE MASON
UNIVERSITY.
PLEASE GO AHEAD.
>>THANK YOU VERY MUCH.
CAN YOU HEAR ME FINE?
I'M GOING TO SHARE MY SCREEN AND
SEE IF THAT WORKS TOO.
IT'S A GREAT PLEASURE AND
PRIVILEGE TO BE HERE TODAY TO
SPEAK.
FROM GEORGE MASON UNIVERSITY.
AND OUR PROJECT TITLE IS THE
PUBLIC REPOSITORY OF NEURAL
MORPHOLOGY.
HOW DATA SHARING BENEFITS USERS
AND PRODUCERS BECAUSE THIS HAS
BEEN VERY NEAR AND DEAR TO MY
HEART FOR MANY YEARS.
MY TEAM MEMBERS WHO HAVE FUELED
AND FOSTERED MUCH OF THE
RESOURCE THAT I WILL HIGHLIGHT
IN THE NEXT TWO MINUTES.
SO THIS IS A DATABASE.
NEURO MORE GO.ORG.
THIS FORMS THE GIGANTIC NETWORK
THAT IS THE BRAIN BUT ALSO THE
SAME TREES ALLOW THE NERVE CELLS
TO INTEGRATE INFORMATION THAT
THEY RECEIVE FROM OTHER NEURONS
AS WELL AS TRANSMIT THAT
INFORMATION BRAIN WIDE TO ALL OF
THE OTHER NERVE CELLS.
AND FOR THE GLEE A THAT TREE
SHAPE ALLOWS THEM TO PERFORM THE
VITAL SUPPORT FUNCTIONS THAT
THESE CELLS ARE TASKED FOR
INCLUDING FORMATION OF MY LINA
ROUND THE ASK ONS OF NEURON AND
IMMUNE REACTION FROM TRAUMA ETC.
NEURO MORPHO.ORG STARTED IN
2006.
TO SHARE DATA TO TRANSLATE IN A
COMMON FORMAT AND POST IT
ON-LINE FOR PUBLIC FREE REUSE
AND ALONG THE WAY WE ADDED MANY
FUNCTIONALITY TO THIS RESOURCE
AND DOCUMENTED THEM IN
SUBSEQUENT PUBLICATIONS.
TODAY USERS CAN ACCESS THROUGH A
SIMPLE WEB PAGE THROUGH ANY
BROWSER AND THEY CAN BROWSE THE
DATA UNDER MAIN CATEGORIES SUCH
AS THE ANATOMICAL REGIONS, CELL
TYPES AND ARCHIVES WHICH ARE THE
SOURCES THAT SHARE THE DATA AND
WITHIN EACH OF THOSE CATEGORIES
THE DISTRIBUTION OF THE DATA IS
FAIRLY EVEN.
THE BULK OF THE DATA COMES FROM
THE MOUSE.
BUT MORE THAN 90 DIFFERENT
SPECIES ARE REPRESENTED.
AND LIKEWISE NEOCORTEX -- IS
VERY MUCH STUDIED AND THIS IS
REFLECTED IN THE AMOUNT OF DATA
THAT WE HAVE AVAILABLE IN THE
REPOSITORY.
AND EVEN FOR THE CELL TYPES.
THAT IS THE CASE.
THERE IS AN EVEN DISTRIBUTION.
DATA CAN BE BROWSED THROUGH
SIMPLE INTERFACES.
THEY CAN ALSO BE SEARCHED AND
ONCE YOU LAND ON A PARTICULAR
PIECE OF DATA OF INTEREST GIVEN
THE RECONSTRUCTION OF THE GIVEN
CELL WHAT USERS HAVE IMMEDIATE
ACCESS TO IS THE ACTUAL FILE
THAT WE CALL THAT THE SWC FILE.
AN AS KEY FILE THAT PROVIDES A
THREE DIMENSION, COORDINATE.
AND THAT FILE IS WHAT ALLOWS
PEOPLE TO DO SECONDARY ANALYSIS.
ILLUSTRATIONS BUT ALSO
SIMULATIONS OF BIOPHYSICAL DATA
AND CONNECTIVITY AND SO FORTH.
IN ADDITION WE ALSO ANNOTATE
EACH OF THE DATA WITH EXTENSIVE
METADATA THAT INCLUDES THE
PROVIDENCE OF THE DATA, THE
EXPERIMENTAL METHODS AND THE
GRAPHIC INFORMATION OFF THE
ORIGINAL PAPERS WHERE THE DATA
WAS FIRST DESCRIBED AND WE
PRECOMPUTE A STANDARD SET OF
MEASUREMENTS.
THE REASON THAT WE DO SUCH
EXTENSIVE ANNOTATION IS NOT ONLY
TO ENRICH THE DATA BUT ALLOW
USERS TO FIND DATA OF INTEREST
BECAUSE ANY OF THE THESE ENTRIES
CAN THEN BE USED IN THE SEARCH
ENGINE.
IMPORTANTLY EACH IS NOT ONLY
ACCESSIBLE BY HUMANS THROUGH
DROP DOWN MENUS AND SO FORTH BUT
ALSO BY MACHINES THROUGH AN
INTERFACE THAT CAN ALSO INQUIRY
THE DATA.
WE ALSO HAVE EXTENSIVE COVERAGE
OF THE LITERATURE NOT ONLY
REGARDING THE DATA THEMSELVES,
ALL OF THE DATA IS ANNOTATED IN
THE DATABASE.
ANY DATA THAT WE IDENTIFY EVEN
IF THE AUTHORS DO NOT RESPOND TO
OUR INVITATION OR DECLINE OUR
INVITATION TO SUBMIT THE DATA.
WE TRACK USAGE OF THE DATA SO
WHEN PEOPLE DOWNLOAD AND USE IT
IN SECONDARY PUBLICATIONS WE
TRACK THAT AS WELL.
AS AN EXAMPLE OF THE DATABASE
REGARDING THE LITERATURE
CONTAINS IDENTIFIERS SUCH AS
PubMed AS WELL AS THE AUTHORS
AND THE STATUS OF THEIR REQUEST.
WHETHER THEIR RESPONSE WAS
RECEIVED AND WHETHER THE DATA
WAS INCLUDED OR NOT AND WE HAVE
A FINITE AMOUNT OF LITERATURE
BECAUSE THE TECHNOLOGY DATES
BACK TO WHEN MICROSCOPES WERE
INTERFACED WITH COMPUTERS ABOUT
40 YEARS AGO.
THE RATE OF ACCUMULATION IS
INCREASING.
THE FIRST HALF OF THE LIFE OF
THE DATABASE 8 COMPLETE YEARS
WERE KIND OF A FLAT LINE.
AND THEN IT PICKED UP.
IF YOU LOOKING AT THE RATE OF
DATA ACCUMULATION THERE IS A 24
FOLD ACCELERATION FROM ABOUT A
THOUSAND TO ABOUT 30,000 PER
YEAR.
SO WE INVESTIGATED WHAT FACTORS
CONTRIBUTED TO THAT
ACCELERATION.
THIS IS A LITTLE GLANCE ON
SOMETHING THAT IS REPRESENTATIVE
OF BIOMEDICAL DATA AT LARGE.
ONE IS THAT THE NUMBER OF PEER
REVIEWED PUBLICATION INCREASED
FROM 120 PER YEAR TO 300.
SO, THAT IS A TWO AND A HALF
FOLD FACTOR.
THE SECOND IS THE AVERAGE
DATASET SIZE INCREASED DUE TO
TECHNOLOGICAL DEVELOPMENT BUT
ALSO INCREASING PRESSURE FROM
THE PEER COMMUNITY BECAUSE
REVIEWERS ARE BECOMING MORE
DEMANDING AS TO HOW MUCH DATA IS
NEEDED.
BUT THE GREATEST FACTOR OF ALL
IS THE ATTITUDE TOWARD DATA
SHARING HAS PLEASANTLY CHANGED.
ABOUT FOUR IN FIVE AUTHORS THAT
WE WOULD CONTACT WOULD EITHER
NOT RESPOND OR DECLINE TO SHARE
DATA WHEREAS IN THE PAST SEVERAL
YEARS THAT PERSONAL HAS
INCREASED TO A VERY CLEAR AND
SIGNIFICANT MAJORITY.
SO WHEN YOU COMBINE THEM ALL
THIS IS ACTUALLY WHERE THIS
CURVE IT IS COMING FROM.
IMPORTANTLY COMBINING THIS ALSO
MEANS THAT A FEW YEARS AGO IN
2019 WE PASSED AN IMPORTANT
LANDMARK WHICH IS MORE THAN 50%
OF THE DATA PRODUCED WORLDWIDE
IS ACTUALLY PUBLICLY AVAILABLE.
AND ANOTHER FACTOR TO THE
QUANTITY OF THE DATA IS THE
FOCUS OF THE DATA FOR THE FIRST
8 YEARS MUCH OF THE
CONCENTRATION OF RESEARCHERS WAS
DENDRITE.
BUT THOSE ARE ALSO THE SMALLER
ARBOR.
WHEREAS IN RECENT TIMES THE
TECHNOLOGY AND SCIENTIFIC
INTEREST HAS SHIFTED TO THE OX
AXE COMESOF THE CELLS.
* SO IT'S NOT JUST THAT MUCH
MORE DATA ARE BECOMING AVAILABLE
BUT A MUCH RICHTER TYPE OF DATA.
OF NOTE ALSO IN 2017 WE ADDED
GLIA.
IT WAS A LATE INTEREST IN THE
FIELD BUT VERY IMPORTANT
ESPECIALLY FOR DISEASE BECAUSE
MOST OF THE DISEASES CORRELATE
WITH CHANGES IN GLIA AND THE
PROPORTION IS INCREASING
DRAMATICALLY AS WELL AND NOW WE
HAVE A REPRESENTATIVE
DISTRIBUTION OF GLIA
RECONSTRUCTIONS FROM DOZENS OF
SPECIES.
RECONSTRUCTION SYSTEMS, CELL
TYPES AND SO FORTH.
SO PART OF THE EFFORT IN
SUPPORTING THIS INCREASE IN THE
DATA IS A PROGRESSIVE AUTOMATION
OF THE PROGRESS SO WE MOVE FROM
LARGELY MANUAL OPERATION TO A
SEMI MANUAL AND HOPEFULLY IN THE
FUTURE FULLY AUTOMATED OPERATION
AND THIS WAS HIGHLIGHTED AT THE
50th ANNIVERSARY WHICH WAS TWO
YEARS AGO.
AND SO PART OF THE PROCESS IS
GOING FROM A VERY MUCH HUMAN
CENTRIC PROCESSING AND USAGE OF
THE DATA TO MORE AND MORE
COMPUTERIZED PIPELINES AND
SERVICES IN AN INDEPENDENT WAY.
BOTH INCREASES THROUGH PUT OF
THE DATA BUT ALSO REDUCES THE
POSSIBILITY OF HUMAN INTRODUCED
MISTAKES IN THE DATA AND ONE
SERVICES WHAT WE CALL A
UNIVERSAL CONVERSION PROCESS
WHERE ALL OF THE SOURCES OF
RECONSTRUCTION CAN NOW BE
TRANSLATED INTO THE OPEN FORMAT.
AND WHEN WE LOOK AT THE USAGE OF
THE DATA WE TRACK BOTH DOWNLOADS
AND HITS AND WE TRACK
STATISTICS.
SO THAT NOT ONLY CONTRIBUTORS
CAN LOOK AT THE USAGE OF THEIR
DATA AND BE ACKNOWLEDGED.
BUT WE CAN TRACK TRENDS AND
EXPERIMENTAL CONDITIONS AND
AGAIN THIS IS CRITICAL FOR THAT
EFFORT.
WHEN WE LOOK AT WHERE THE DATA
ARE GOING.
NOT SURPRISINGLY THE MAJORITY OF
THE DATA GO TO THE SO-CALLED
WHAT USED TO BE CALLED FIRST
WORLD OR HIGH INCOME NATIONS.
THE EUROPEAN UNION AND AMERICA.
ABOUT A THIRD GOES TO THE
INTERNATIONAL MONETARY FUND AND
THEN TO THE LOWEST INCOME
COUNTRIES.
THIS DISTRIBUTION ACTUALLY
BECOMES MORE INTERESTING WHEN
YOU LOOK AT WHERE THE DATA ARE
COMING FROM.
SO THE UPLOADS INTO THE DATABASE
AND WE FIND THAT 80% OF THE
RECONSTRUCTIONS ARE NORTH
AMERICA AND THE EU AND NONE IN
AFRICA AND THE LOWER INCOME
COUNTRIES.
THE RESOURCE AND THE DATA
ON-LINE FOR FREE CREATES A FLOW
OF KNOWLEDGE ACCESS FROM THE
DATA PRODUCERS TO DATA USERS IN
A VERY GLOBAL WAY AND INCREASES
ACCESS INDEPENDENT OF NATIONAL
INCOME.
SO WHAT ARE THE DATA USED FOR.
WE TRACK THE LITERATURE.
SO WE HAVE A GOOD SENSE OF THAT.
IN ADDITION TO SCIENTIFIC
RESEARCH THERE HAVE BEEN
COMPETITIONS INCLUDING EARLY ON.
THE MAJOR SCIENCE OLYMPIA EVENT
IN CHINA IN 2011 AND NIH AND THE
HOWARD HUGHES SPONSORED
COMPETITION.
MANY ART EXHIBITs IN ALL SORTS
OF MEDIA AND INCREASING
EDUCATIONAL USE ABLE INCLUDING
BOOKS, MASSIVE ON-LINE COURSES
AS WELL AS ON-LINE EDUCATION AT
ALL LEVELS.
THAT REALLY EXPLODED DURING THE
COVID YEARS AND IS NOW
CONTINUING.
WE HAVE A LITTLE INFORMATION ON
NIH GRANTS THAT ARE BEING
SUBMITTED THAT PROPOSE TO USE
THE DATA.
HUNDREDS OF GRANTS FROM HUNDREDS
OF PPs AND INSTITUTIONS AND
GRAND TOTAL OF GOING TOWARDS
BILLION DOLLAR INVESTMENT AND
ABOUT 40% SUCCESS RATE WHICH IS
HIGHER THAN THE NIH AVERAGE.
AND THEN ALSO COMMERCIAL TECH
RESEARCH AND DEVELOPMENT
INCLUDING IMAGING SOFTWARE.
SO WITH RESPECT TO BIOMEDIC
RESEARCH MORPHOLOGY PROVIDES THE
FINGERPRINTING SIGNATURE.
MANY PEOPLE RECONSTRUCT THE
NEURON TO UNDERSTAND WHAT CELL
TYPES THEY ARE.
THAT THEY ARE LATCHING ON.
THERE IS A HUGE AMOUNT OF
COMPUTATIONAL MODELING.
TO UNDERSTAND THE BIOPHYSICS.
A LOT OF COMPARATIVE ANALYSIS ON
THE GEOMETRY TO COMPARE NOT ONLY
DISEASE CONDITIONS FROM
PHYSIOLOGICAL STATES BUT ALSO
DEVELOPMENTAL CHANGES AND CROSS
SPECIES COMPARISONS BETWEEN
ANATOMICAL REGIONS AND CELL
TYPES ETC.
MORE RECENTLY THE UNDERSTANDING
OF CIRCUITS THAT CAN BE
QUANTIFIED FOR THE POSSIBILITY
OF CELLS TO CONNECT TO EACH
OTHER.
AND THEN ALSO DATA MINING AT
LARGE AND EDUCATION AND
OUTREACH.
SO AS EXAMPLES THESE ARE SOME OF
THE EARLY STUDIES OF DISCOVERIES
BOTH IN RODENTS AND IN HUMANS A
AND RELATED COMPUTATIONS BASED
ON THE EXPERIMENTAL DATA.
MUCH MORE RECENT AND WE'RE
COMING IN THE RECENT YEARS OF
THE ADVENT OF HERO DENSITY NEURO
PIXEL PROBES THAT WERE TESTED IN
RODENT MODELS PEOPLE HAVE
STARTED IMPLANTING IN HUMANS.
THEY HAD TO DO EXTENSIVE
SIMULATIONS OF WHAT WAS THE
RIGHT ANGLE OF IMPLANT OF THOSE
PROBES BEFORE STARTING TO DRILL
IN THE SKULL AND LIVE PATIENTS.
THE COMPUTATION OF RADIATION
DAMAGE.
BOTH TO UNDERSTAND THE SIDE
EFFECTS OF RADIATION THERAPY AND
ALSO TO PREPARE FOR INTEREST
PLAN TORI TRAVEL SINCE NASA IS
NOW PLANNING ACTIVELY FOR HUMANS
ON MARCHES POTENTIALLY IN OUR
LIFETIME.
SO THE NUMBER OF PUBLICATIONS
WILL BE TOO HIGH FOR EVEN A LONG
DAY LET ALONE A FEW MINUTES BUT
WE'RE TALKING ABOUT THOUSANDS OF
PUBLICATIONS AND ALL MAJOR
JOURNALS.
IN 2022 WE HAD THE VERY NICE
MILESTONE OF THE NUMBER OF
SECONDARY PUBLICATIONS
SURPASSING THE NUMBER OF PRIMARY
PUBLICATIONS AND THE NUMBER OF
CITATIONS IS CREEPING UP TO NOW
50% OF THOSE OF THE PRIMARY
PUBLICATIONS.
THIS IS QUITE IMPRESSIVE.
THE SECONDARY PUBLICATIONS COME
AFTER THE PRIMARY PUBLICATIONS.
AT THE MINIMUM SHARING OF THE
DATA INCREASED THE IMPACT UP TO
150% OF THE ORIGINAL SOURCES AND
I FIND THAT VALUABLE AND
IMPRESSIVE.
WHAT IS THE ADVANTAGE FOR THE
DATA PROVIDERS.
IT TURNS OUT THAT SHARING DATA
PROVIDES AN AVERAGE 13% INCREASE
IN CITATIONS AND THIS TRANSLATES
INTO THE NICE THINGS THAT
ACCOUNT FOR PROMOTION, TENURE,
AWARDS ETC.
AND WE'RE GOING TO RELEASE THE
ANALYSIS BUT ALSO AN AUTHOR
REPORTING SERVICE SO THAT
AUTHORS WILL BE NOTIFIED WHICH
WILL PROVIDE FURTHER INCENTIVE.
THEIR DATA IS NOT ONLY PUBLISHED
BUT REUSED IN THE COMMUNITY.
I'LL STOP HERE BUT NOT BEFORE
THANKING THE REALLY INSTITUTIONS
AND PEOPLE WHO ENABLED THIS WORK
OVER THE YEARS.
MOST AND FOREMOST NIH THAT
FUNDED THIS WORK FROM THE VERY
GET-GO.
THE DEPARTMENT OF ENERGY.
IN GREEN ARE THE ACTIVE GRANTS.
A VALIANT GROUP OF TRAINEES AND
THE GROWTH HAS BEEN THRUSTED.
>>THANK YOU VERY MUCH.
CLEARLY A REMARKABLE PLATFORM
AND EXAMPLE OF THE POWER OF DATA
SHARING.
BEFORE WE GET TO THE QUESTIONS
FIRST WE'LL MOVE ON TO OUR NEXT
SPEAKER.
EUN-YOUNG MUN.
WHO IS A PROFESSOR AT NORTH
TEXAS UNIVERSITY.
>>HELLO.
MY VIDEO IS STOPPED BY THE
ORGANIZER.
>>LET'S SEE.
I'LL SHARE THE SCREEN.
CAN YOU SEE MY SCREEN?
>>YES, WE CAN.
>>OKAY.
THANK YOU FOR THE OPPORTUNITY TO
PRESENT OUR WORK.
I'M EUN-YOUNG MUN.
I WANT TO THANK NATIONAL
INSTITUTE OF ALCOHOL ABUSE AND
ALCOHOLISM.
VERY HAPPY TO SHARE OUR
EXPERIENCE IN THIS SYMPOSIUM.
THE NEXT SLIDE PROVIDES AN
OUTLINE FOR THE TALK.
THE PROJECT BACKGROUND AND
OUTCOMES.
PROJECT INTEGRATE WAS LAUNCHED
IN 2010 AND WE OBTAINED
INDIVIDUAL PARTICIPANT DATA FROM
24 TRIALS.
12,000 CASES AT BASELINE.
WE ARE ONE OF THE FIRST
PROJECTS THAT EMBRACE DATA
INTEGRATION CHALLENGES.
IN ADDITION WE COMMUNICATED WITH
THE ORIGINAL INVESTIGATORS WHEN
WE HAD A QUESTION AND THEY WERE
VERY GENEROUS WITH THEIR TIME.
7 WE THEN ANALYZED DATA.
OUR GOALS WERE TO PROVIDE LARGE
SCALE EVIDENCE.
THUS FAR WE HAVE CONTRIBUTED
OVER 65 ARTICLES.
WE SUPPORTED 7 POSTDOCS.
WE DEVELOP YouTube CONTENT.
WE HAD FUN MAKING IT AND I GUESS
WHAT IS IMPORTANT HERE IS SINCE
2019 WE STUDIED ALSO SHARED AND
IDENTIFIED DATA.
OUR FIRST DATA THAT WE SHARED IN
2019 HAS HAD HAD ALMOST 900
VIEWERS.
WE HAVE USED DATA IN THE SPACE.
IT HAS BEEN USED AS A TRAINING
DATASET.
OR AS A REAL DATASET TO PASS NEW
STATISTICAL MODELS.
OTHERS HAVE DOWNLOADED DATA.
ULTIMATE LEADS WE CAN SPEED UP
SCIENTIFIC DISCOVERIES.
SO THE TEAMS THUS FAR 124 UNIQUE
INDIVIDUALS HAVE CONTRIBUTED TO
THE PROJECT WITH 10 INDIVIDUALS
BEING MOST ACTIVE AND CURRENTLY
ON THE PROJECT AND -- UNIVERSITY
OF OREGON, UNIVERSITY OF HONG
KONG.
UNIVERSITY OF WASHINGTON.
WE ALSO WANT TO SHOW THE LIST OF
INVESTIGATORS.
MOST OF THEM ARE ACTIVE IN THE
FIELD AND HAVE SUPPORTED THE
PROJECT.
I EXPRESS MY APPRECIATION FOR
THEIR CONTRIBUTION.
SINCE THIS IS A CELEBRATORY
SYMPOSIUM TO SHOWCASE THE
SUCCESS OF DATA REUSE AND
SHARING I WILL SHOW YOU SOME
GRAPH HERE.
THIS SLIDE SHOWS A NETWORK OVER
TIME.
THE FIRST TWO YEARS EACH -- IT
WAS SPARSE AND LIMITED.
EACH IS SHOWN AS A RED NODE AND
THE COLLABORATION AMONG AUTHORS
ARE THE BLUE EDGES.
OVER THE YEARS IT HAS BECOME
QUITE ENLARGED AND WI --
STRENGTHENED CONNECTIONS OVER
TIME.
THE GREEN NODES PUNILY
INDICATION FROM A SUPPORTED
PROJECT AND THE RED CITATIONS.
THE CONNECTIONS HAVE BEEN PRETTY
WEAK BUT NOW AT THE END' LAST
YEAR THE CONNECTIONS HAVE GOTTEN
STRONGER AND IT HAS GOTTEN QUITE
LARGE.
OVER-ALL THESE TWO SLIDES
SUGGEST THE PROJECT AND IMPACT
ON THE FIELD HAVE GROWN.
THE SUCCESS OF THE PROJECT
INTEGRATE MAY BE DUE TO OUR TEAM
SCIENCE APPROACH.
THE MEMBERS BRING A WIDE RANGE
OF DOMAIN OF EXPERTISE.
NEXT I WANT TO SHOW YOU SOME
SELECT OUTCOMES THAT WE'VE
PRODUCED.
IN TYPICAL META-ANALYSIS IT'S
IMPORTANT TO HAVE THE SAME -- OR
EQUIVALENT MEASURES OF STUDIES.
IN THE CASE OF BRIEF ALCOHOL
INTERVENTIONS -- HOW MANY
STANDARD DRINKS ONE HAD IN A
TYPICAL WEEK.
0 MEANS NO DRINK.
AND IT'S A NICE MEASURE WHERE
EVERYBODY USES AND IT'S THE SAME
MEASURE ACROSS OUR STUDIES
HOWEVER THE HISTORIC GRAM OF THE
NUMBER OF DRINKS, OUTCOME
DISTRIBUTIONS SIGNIFICANTLY
DIFFER BY STUDIES.
RESULTING IN A NEED TO ANALYZE
DATA.
SO MOST STUDIES OF A ZERO
INFLATION AND MANY HAVE THIS
VERSION.
IF YOU LOOK AT THE STUDY SIDE BY
SIDE STUDY ONE HAD EXCESSIVE 0
HERE AND STUDY 12 TARGETED HEAVY
DRINKING INDIVIDUALS.
IT ACQUIRES DIFFERENT
STATISTICAL MODELS TO BE USED TO
ANALYZE AN AVERAGE OVER-ALL
EFFECT SIZE OF -- OUTPUT
INNOVATIONS.
WHEN WE USED MARGINALIZE ENDS
AND INFLATED MODELS WE CAN
INTERPRET INTERVENTION EFFECT
DESPITE INFLATION AND WITH THIS
INNOVATION WE CONCLUDED THE
BRIEF INNOVATION HAD 8% OF THE
ADVANTAGE RIGHT HERE.
THROUGH SIX MONTHS POST
INNOVATION.
COMPARED TO CONTROLS.
SO THIS WAS AN EXAMPLE THAT
THROUGH DATA REUSE WE DREW A
MORE DETAILED CLINICAL
MEANINGFUL INTERPRETATION.
WE PRODUCE A LONG LASTING
BENEFIT OF FACTS.
THIS SLIDE SHOWS SOME STUDIES
TARGETED RELATIVELY LOW.
THE OVER-ALL -- MEAN NUMBER OF
DRINK REQUESTS IS WHATS ABOUT 9
PER WEEK.
HOWEVER ON AVERAGE THOSE -- IT
CAN BE CHALLENGING ESPECIALLY IF
IT'S A FACT -- LIMITED.
HEAVIER DRINKING INDIVIDUALS IN
STUDIES IN 11 AND 15 MAY NOT
MEET THE STUDY INCLUSION.
SO IF THE TRIAL STATES THAT
PREINNOVATION -- THIS SHOULD BE
INTERPRETED WITH A CAVEATS.
MEAN NUMBER OF DRINKS THAT ARE
DIFFERENT FOR MEN AND WOMEN.
BECAUSE MEN DRINK MORE THAN
WOMAN.
SO BY KEEPING THESE ELEMENTS
APART -- WE ARE CURRENTLY
EXAMINING A BASELINE USING
META-ANALYSIS.
THIS IS -- FOR FEMALE CAN GO
STUDENTS -- EFFECTIVE AROUND SIX
DRINKS PER WEEK AND 18 DRINKS A
WEEK.
SO BASED ON THIS FINDING BRIEF
ACCURATE INNOVATIONS WOULD BE
SUITABLE FOR ANYONE WHO DRINKS
SOCIALLY.
HOWEVER GIVEN THE WIDE RANGE OF
CONFIDENCE INTERVALS A BETTER
DESIGN STUDY MAY BE NEEDED FOR
OTHERS WHO DRINK MORE.
I HAD REGULAR TALK WITH A
PROJECT INTEGRATING TEAM.
THOSE WHO SHARE DATA KNOW THEIR
VALUABLE DATA GET EXTENDED.
AND IT HAS HELPED THEM TO
UNDERSTAND THEIR DATA.
FROM OUR END WE HAVE DEVELOPED
NEW RESEARCH QUESTIONS PROVIDED
LARGE SCALE EVIDENCE AND
DEVELOPED NEW METHODS AND IDEAS.
THIS IS ALL IN COLLABORATIVE
SITUATIONS.
IT IS NECESSARY TO PROVIDE MORE
EDUCATION AND MORE HELPFUL
TOOLS.
INCENTIVES TO TRACKING
CONTRIBUTIONS TO SCIENCE.
THANK YOU FOR LISTENING TO THIS
TALK.
IF YOU HAVE ANY QUESTIONS YOU
CAN I --
>>YES.
WE CAN MOVE FORWARD WITH A FEW
QUESTIONS.
THERE ARE A FEW QUESTIONS IN THE
CHAT.
THANK YOU VERY MUCH FOR THOSE.
FIRST ONE TO GEORGIO.
I THINK THEY ARE QUITE
INTERESTED -- SORRY.
I'M JUST MOVING THROUGH THESE
NOW.
TELL US MORE ABOUT THE ATTITUDES
HOW THE ATTITUDES YOU THINK
SHIFTED IN DATA SHARING AND
WHERE YOU SEE THAT MOVING
FORWARD.
THE GRAPH YOU SHOWED GEORGIO IS
QUITE REMARKABLE IN THE UP TAKE
AND THE TIME THAT IT STARTED.
YOU MENTIONED SOME OF THE
CRITERIA AT REASONS FOR THAT.
MAYBE YOU CAN EXPOUND ON THAT
AND ANY INSIGHTS YOU MIGHT HAVE
ABOUT SEEING THAT MORE BROADLY
ACROSS DATA SHARING
>>YES.
I HAVE SOME EVIDENCE BASED
INFORMATION ON THIS.
THE FIRST THING TO NOTICE IS
THAT UNLIKE SOME OF THE VERY
IMPORTANT CONSIDERATIONS THAT
WERE DISCUSSED EARLIER TODAY
THERE IS NO ISSUE OF PRIVACY.
THESE ARE NEURONS THAT ARE
RECONSTRUCTED LARGELY FROM
ANIMALS AND NEURONS
RECONSTRUCTED FROM HUMAN
PATIENTS AND HUMAN TISSUE.
THERE IS NO WAY TO IDENTIFY
INDIVIDUALS BASED ON THE SHAPE
OF INDIVIDUAL NEURONS IN ANY
WAY.
SO THERE IS NO ISSUE OF PRIVACY.
THERE IS NO ISSUE OF IP IF THE
DATA ARE PUBLISHED IN BIOMEDICAL
JOURNALS.
HOWEVER THERE IS AN ISSUE OF
OWNERSHIP.
THE ISSUE CAME UP MANY TIMES.
WHO'S OWNING THE DATA?
DOES THE DATA BELONG TO THE
RESEARCHERS.
TO THE INSTITUTIONS THAT THE
RESEARCHER WORKS FOR OR TO THE
PUBLIC TO THE TAXPAYERS TO THE
FUNDERS.
7 ULTIMATELY THERE IS A MATTER
OF PERSONAL INTEREST BECAUSE
REASONS THAT RESEARCHERS DO NOT
WANT TO SHARE THEIR DATA IT
TAKES TIME TO SHARE DATA.
SOMETIMES THE PAPERS ARE
PUBLISHED YEARS AFTER THE DATA
ARE COLLECTED.
THOSE DATA MAY NOW BE IN BACK
UPS AND IT MAY TAKE TIME TO
RETRIEVE THEM.
THE POSTDOCS AND GRAD STUDENTS
WHO COLLECTED THOSE DATA MIGHT
HAVE LEFT FOR THEIR NEXT JOB AND
THE PI MAY NOT HAVE BEEN IN
TOUCH WITH THE DATA. THERE IS
ALSO AN ISSUE OF SOME FEAR.
FEAR OF BEING SCOOPED WITH THEIR
OWN DATA.
FEAR OF BEING PROVEN WRONG.
OF SIMPLY INCREASING THE
COMPETITION.
IT TOOK MANY YEARS TO COLLECT
THIS DATA AND SOMEONE IS GOING
TO REUSE MY DATA IN A PAY OFF
WAY.
SO WHY SHOULD I GIVE SOMEBODY
ELSE AN ADVANTAGE.
THEY SHOULD GO AND COLLECT THEIR
OWN DATA.
SO THERE ARE REASONS THAT ARE
VERY UNDERSTANDABLE FROM THE
POINT OF VIEW OF HUMAN
PSYCHOLOGY.
WE HAVE TO LOOK AT OUR HEARTS
AND THINK OF WHAT WE COLLECT
DATA FOR.
IT'S NOT THAT WE DO THAT JUST
FERRYING OH AND JUST FOR GLORY.
WE BELIEVE IN RESEARCH AND
PUSHING THE PROGRESS THE
ENVELOPE OF SCIENCE AND THE
STATE-OF-THE-ART OF KNOWLEDGE
AND THERE IS NO DOUBT IN
ANYONE'S MIND THAT SHARING THE
DATA ACTUALLY IMPROVES THAT AT A
SOCIETAL LEVEL AT LEAST AT AN
INDIVIDUAL LEVEL.
SO WHAT CHANGED?
I THINK MANUFACTURERS.
ONE IS A GENERATIONAL SHIFT.
SOME FAMOUS SCIENTISTS SAID A
TRULY REVOLUTIONARY SCIENTIFIC
THEORIES, THE OLD PEOPLE DIE AND
THE YOUNG PEOPLE STUDY THEM IN
TEXTBOOKS.
THERE IS NO DOUBT THAT OLDER
PIs ARE MORE HESITANT TO SHARE
DATA. YOUNGER PEOPLE, THEY
LIVED IN THE WORLD OF OPENNESS
INTERNET FROM THE GET-GO FROM
WHEN THEY WERE BORN AND THERE IS
NOT EVEN A QUESTION.
OF COURSE THE DATA ARE GOING TO
BE PUBLIC.
THE SECOND ISSUE HAS TO DO WITH
EVIDENCE OF THE SUPPORT.
PEOPLE ARE NOW LOOKING MORE AND
MORE AT CITATIONS TO THEIR PAPER
AND TO HOW MANY PAPERS ARE
PUBLISHED.
SO IF THERE IS EVIDENCE THAT
SHARING THE DATA INCREASES THE
IMPACT THROUGH CITATIONS IS AN
INCENTIVE.
AND THE THIRD ONE IS PRESSURE
FROM AGENCIES ARIZONA JOURNALS.
NIH HAS A SHARING MANDATE.
JOURNALS ARE REQUESTING THAT AND
REVIEWERS ARE REQUESTING THAT.
THAT'S AN INCH AT A TIME SHIFT
IN THE TIDE.
>>THE NOTION OF CHANGE HAPPENS
ONE RETIREMENT AT A TIME IS A
GOOD ONE AND I CERTAINLY
RESONATE WITH THAT.
EUN-YOUNG, DO YOU HAVE ANY
THOUGHTS ABOUT THAT.
SORT OF PERCEPTIONS AND
WILLINGNESS TO SHARE CHANGE OVER
TIME AND FOR INTEGRATE AND YOUR
WORK
>>YES.
I ECHO EVERYTHING.
I SAY THE GENERAL EXPECTATION
HAS SHIFTED.
BUT NONETHELESS WHEN SOMEBODY GO
AFTER INVESTIGATORS AND CLAIMS
THAT YOU ARE SUPPOSED TO SHARE
DATA IT'S REALLY NOT PLEASANT.
SO WE TRY TO PROMOTE GOOD
RELATIONSHIPS WITH EVERYBODY.
SO OF COURSE IT'S A MUCH SMALLER
SCALE.
BUT I TRY TO EMPHASIZE -- AND IT
HAS BEEN PRETTY GOOD.
AND GOING FORWARD WE THINK THAT
WE ONLY INCREASE.
BUT I ALSO AGREE WITH GEORGIO.
THERE HAS TO BE BETTER
INCENTIVES.
WHEN WE SHARE DATA IT TAKES A
LOT OF EFFORT TO SHARE DATA.
SO THERE HAS TO BE SOME BETTER
RECOMMENDATIONS FOR SHARING DATA
RATHER THAN JUST A REQUIREMENT.
>>WELL, THE NOTION GEORGIO, I
MENTIONED THE FACT THAT
CITATIONS CAN CHANGE TOO.
IT'S NOT JUST THE PUBLICATIONS.
7 PEOPLE ARE MORE AND MORE
COMING IN TUNE WITH.
DO YOU ISSUE -- DOIs FOR YOUR
DATA?
>>UPON REQUEST.
WE DON'T DO THAT SYSTEMATIC
CALY.
>>UPON REQUEST.
WE DO NOT DO IT BY DEFAULT
BECAUSE WE RELY ON THE LIBRARY
SERVICE OF GEORGE MASON
UNIVERSITY.
AND IT'S A COST SERVICE.
A SMALL TOKEN BUT WE CANNOT DO
IT SYSTEMATICALLY UNLESS IT
BECOMES A REQUIREMENT OR
CONSTANT REQUEST.
THEN WE WOULD HAVE TO ASK FOR
SUPPLEMENT REFUNDING.
*.
>>HERE IS A QUESTION I'M GOING
TO READ.
VERY INTERESTING WAYS TO
ILLUSTRATE COLLABORATION
AUTHORS.
HOW DO YOU HOPE THE IMAGE WILL
EVOLVE OVER THE NEXT FIVE YEARS?
>>I HOPE THAT THIS NETWORK SIZE
INCREASE FROM DIFFERENT SOURCES.
WE CAN SEE -- WE CAN COLOR IT
DIFFERENTLY BUT WE WANT TO
ORGANICALLY GROW AND GET MORE
COMPLEX AND WITH DIFFERENT
EXPERTISE COMING IN.
WE ACTIVELY COLLABORATE WITH
MANY INVESTIGATORS IN DIFFERENT
FIELDS AND IT HAS BEEN VERY,
VERY GOOD FOR THE PROJECT
INTEGRATE.
>>GREAT.
THANK YOU FOR THAT.
AND MAYBE WE HAVE A BIT OF TIME
SO I'M WONDERING HOW YOU EACH
LEARNED ABOUT THE PRIZE.
>>I HAVE LEARNED FROM MAILING
LISTS.
I ACTUALLY RECEIVED AN E-MAIL I
THINK DAYS AFTER THE PRIZE WAS
ANNOUNCED ORIGINALLY THROUGH A
PRESS RELEASE FROM NIH FASEB.
AS SOON AS I SAW IT I WAS LIKE
WOW!
BEFORE I EVEN REALIZED I SHOULD
APPLY TO THAT.
AND BEFORE CONTACTING MY TEAM
MEMBERS, WHAT DO YOU THINK.
IS THAT A GOOD FIT OR WHAT?
THE FIRST REACTION WAS ELATION.
THERE IS RECOGNITION FOR DATA
SHARING.
THAT IS REALLY GOOD BECAUSE PART
OF THE ISSUE FOR BOTH PEOPLE
SHARING DATA AND RESOURCE
PROVIDERS THAT CREATE THE
MEDIUMS IT'S A LIKE OBSCURE
BEHIND THE CURTAINS WORK.
IT'S NOT THE SAME AS DISCOVERY
SCIENCE.
VERY CLEAR FRONT SCIENCE.
SO THE FACT THAT THERE WAS A
MOVEMENT IN THAT REGARD IT WAS
VERY, VERY PLEASING I HAVE TO
SAY.
AND THEN IT TOOK A LITTLE BIT TO
SAY -- WE WOULD BE A GOOD
CANDIDATE FOR THIS AND WITHIN
DAYS OF CONTACTING MY TEAM
MEMBERS I STARTED RECEIVING
DOZENS OF SPONTANEOUS E-MAILS
FROM COLLEAGUES OR DATA
PROVIDERS OR DATA USERS SAYING
THERE IS THIS OPPORTUNITY HERE.
YOU GUYS SHOULD DEFINITELY DO
IT.
SO THAT WAS REINFORCING.
>>I HEAR YOU.
IT CERTAINLY CAN BE CAN FEEL
LIKE AN ALTRUISTIC ENDEAVOR TO
SUPPORT NEURO MORPHO FROM THE
INSIDE -- BUT THE BENEFITS ARE
BECOMING MORE AND MORE MORE APP.
EUN-YOUNG FROM YOUR SIDE, HOW
DID YOU LEARN ABOUT THE PROJECT?
>>I ALSO RECEIVED AN E-MAIL BUT
I DON'T KNOW FROM WHO.
AND I WAS THINKING THIS IS AS
YOU STATED -- REUSE AND SHARE
THE DATA.
THIS IS IT.
WE COMPETED FOR DATA REUSE.
AND I GUESS IN RESPONSE TO THE
PREVIOUS COMMENT, WE SHARED DATA
IN GENERAL REPOSITORY AND THEY
HAVE A DUI PROVIDED FOR EACH.
SO WE HAVE SO FAR 10 DATASETS
UPLOADED WITH THE CODE AND THE
FUNCTIONALITY IN THE GENERAL
REPOSITORIES AND MY EXPERIENCE
HAS BEEN PRETTY PLEASANT.
>>THE CHALLENGE IS DOING THAT
AT SCALE WHICH IS GEORGIO'S
ISSUE.
YOUR DATASETS ARE QUITE
DIFFERENT IN THAT THEY ARE QUITE
HIGH VALUE INTELLECTUALLY AND
TAKE A LOT OF TIME TO GET TO
WHAT YOU'RE SHARING.
FEWER BUT VERY HIGH VALUE.
7 AND SO, ITS EASIER TO IMAGINE
THAT.
WHEN YOU HAVE TENS OF THOUSANDS
CURRENTLY THE WAY THEY ARE
ISSUED IS TOO LABOR-INTENSIVE.
THAT IS SOMETHING THAT MAY NEED
TO BE CONSIDERED GOING FORWARD.
HOW WE DO CREATE THAT PERMANENT
IDENTIFIER AT SCALE HOW WOULD
YOU DO THAT ACROSS TENS OF
THOUSANDS OF ENTRIES IS AN
ISSUE.
IT'S QUITE INTERESTING, WE HAVE
A FEW MINUTES LEFT.
I THINK THAT IS ALL THE
QUESTIONS THAT WERE IN THE CHAT.
SO ONE OF THE QUESTIONS WAS HOW
TO ADDRESS THE QUESTION OF OPEN
PUBLICATION FEES.
SOMEONE IS ASKING ABOUT HOW WE
MIGHT MANAGE THOSE ISSUES.
AND THE YEAR OF OPEN SCIENCE AND
OPEN PUBLICATIONS OF COURSE WE
CREATE NEW BARRIERS.
IN THE VERY OLD DAYS OF
PUBLICATIONS YOU KNOW IT WAS A
SUBSCRIBER MODEL.
SUBSCRIBERS PAID FOR THE FEE.
WHEN WE MOVED TO OPEN ACCESS WE
CREATED A PRODUCER'S FEE.
THOSE THAT PRODUCE THE DATA PAY
THE MONEY SO WE'VE SHIFTED WHO'S
PAYING AND SOMETIMES THAT IS A
BARRIER.
THE NOTION OF OPEN SCIENCE AND
OPEN PUBLICATION.
FOR THOSE WHO HAVE TROUBLE
FINDING THOSE FEES AND WHEN YOU
COME BACK I MENTIONED THE
DEVELOPING WORLD AND HOW
CRITICAL IT SEEMS TO BE THAT
EVEN THOUGH IT COSTS TREMENDOUS
AMOUNTS OF MONEY TO HAVE THE
PLATFORMS TO CREATE THE DATA
THAT IT'S BEING USED BY THOSE
OTHER AREAS OF THE WORLD.
IT'S THE SAME THING IN TERMS OF
THOSE OPEN FEES.
SO, THAT IS CERTAINLY AN ISSUE.
I SORT OF TALKED TO THAT ONE.
IT WAS MORE OF A DISCUSSION
POINT.
GEORGIO, HOW DOES NEURO MORP
MORPHO.ORG KEEP TRACK OF THE
TRENDS.
DO YOU GOES WITH WHAT HAPPENS?
DO YOU SEE WHAT HAPPENS?
IT'S ALL TECHNOLOGY DRIVEN.
>>WE USED TO DO IT THE OLD
FASHIONED WAY.
PubMed SEARCHES AND EXCEL
SPREADSHEETS.
IT WAS NOT SUSTAINABLE IN PART
BECAUSE OF THE SCALE.
IT DOESN'T REALLY SCALE.
THE QUALITY CONTROL ON THAT IS
QUITE DIFFICULT TO UPHOLD.
7 WE NOW MOVE TO BASICALLY
MACHINE LEARNING TECHNOLOGIES
THAT WE CUSTOM DEVELOPED
LEVERAGING APPLICATION
PROGRAMMERS INTERFACE FROM ALL
MAJOR PUBLISHERS.
SO NOW WE CAN THROW CONTINUOUS
LITERATURE SEARCHES USING KEY
WORDS SO CASTING THE NET VERY
WIDE.
AND THEN WE HAVE A VERY NICE
FULL TEXT DEEP LEARNING MODEL
THAT DOES A PRETTY GOOD JOB AT
CATALOGING WHICH PAPERS MIGHT
CONTAIN INTEREST.
ULTIMATELY YOU NEED HUMANS TO
LOOK AT THE PAPERS AND DETERMINE
IF IT IS REAL OR NOT BUT IF YOU
DO IT FOR A FEW DOES PAPERS A
WEEK IT'S DOABLE AND YOU CAN
START WITH HUNDREDS OF THOUSANDS
OF PUBLICATIONS THEN IT'S NOT
DOABLE.
8 DOMAIN EXPERTISE AND HUMAN
KNOWLEDGE AND SOME BRUTE FORCE
MACHINE CRANKING UNDERNEATH.
>>GOT IT.
WE'RE JUST ABOUT OUT OF TIME.
IT WAS INSIGHTFUL.
VERY DIFFERENT KINDS OF
PLATFORMS BUT IN EACH CASE DATA
SHARING HAS BEEN CRITICAL AND
IT'S GREAT TO SEE THE
TRANSITIONS.
AND HOPEFULLY WE'LL SEE IT
CONTINUE.
I'M NOW GOING TO TRANSITION TO
YVETTE SEGER.
>>THANK YOU, PARKER.
THANK YOU ALL FOR JOINING US FOR
DAY ONE.
TODAY'S PANEL FEATURES A RANGE
OF OUTSTANDING RESEARCH.
I WANT TO THANK ALL OF TODAY'S
PRESENTERS FORGIVE APPROXIMATING
US A WINDOW INTO THEIR
INCREDIBLE TEAM EFFORTS AND I
APPRECIATED SO MANY OF THEM
SHOWING THEIR TEAM MEMBERS.
7 AND AS WELL AS OUR MODERATORS
FOR SHEPPARDING US THROUGH THE
CONVERSATIONS.
I WANT TO THANK OUR AUDIENCE FOR
THEIR THOUGHTFUL QUESTIONS
THROUGHOUT THE DAY.
WE HOPE THAT MANY OF YOU WILL
JOIN US TOMORROW FOR DAY TWO
WHICH WILL FEATURE THREE MORE
PANELS.
SHOULD BE ANOTHER EXCITING DAY.
WE'LL GET STARTED AT 11:00 A.M.
EASTERN AND THE SESSION WILL BE
LIVE STREAMED ON THE NIH VIDEO
CAST.
BEFORE WE LOG OFF FOR THE DAY I
WANT TO ACKNOWLEDGE OUR
COLLEAGUES IN THE NIH WAS OF
DATA SCIENCE STRATEGY.
HOPE TO SEE MANY OF YOU TOMORROW
AND THANK YOU SO MUCH.