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Extracting Information from Large Datasets

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Air date: Tuesday, May 13, 2003, 2:00:00 PM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 33 * This only includes stats from October 2011 and forward.
Category: Special
Runtime: 01:05:33
Description: Current "-omic" techniques are able to produce large amounts of data for a relatively small number of samples in different disease-states. The goal of on-going investigations at the Advanced Biomedical Computing Center (ABCC) is to identify specific features from these datasets to classify the state of an unknown sample with high sensitivity and selectivity. Unfortunately, the amount of data available for each sample is so large that random noise can be used to separate one class of samples from another with virtually 100% accuracy. Such a numerical model has very good statistics, but very little information content. Our efforts are designed to bridge the gap between purely numerical models and classification models that use key features that may suggest an underlaying biological mechanism.

For more information, visit the Mass Spectrometry Interest Group of the NCI at Frederick
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NLM Title: Extracting information from large datasets [electronic resource] / Brian Luke.
Author: Luke, Brian.
National Institutes of Health (U.S.)
Subjects: Computational Biology
Information Storage and Retrieval
Publication Types: Webcasts
Rights: This is a work of the United States Government. No copyright exists on this material. It may be disseminated freely.
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NLM Classification: QU 26.5
NLM ID: 101267830
CIT Live ID: 2453
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