Extracting Information from Large Datasets |
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| Launch in standalone player | |
| Air date: | Tuesday, May 13, 2003, 2:00:00 PM |
| Category: | Mass Spectrometry Interest Group of the NCI at Frederick |
| 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 |
| Author: | Brian Luke, Advanced Biomedical Computing Center, SAIC |
| Runtime: | 90 minutes |
| Rights: | This is a work of the United States Government. No copyright exists on this material. It may be disseminated freely. |
| CIT File ID: | 10743 |
| CIT Live ID: | 2453 |
| Permanent link: | http://videocast.nih.gov/launch.asp?10743 |