A Bayesian probabilistic approach to transform public microarray repositories into disease diagnosis databases
Duration: 1 hour 7 mins 14 secs
About this item
| Description: |
Huang, H (UC Berkeley)
Friday 04 April 2008, 14:00-15:00 High Dimensional Statistics in Biology |
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| Created: | 2008-04-11 16:15 | ||||
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| Collection: | Statistical Theory and Methods for Complex, High-Dimensional Data | ||||
| Publisher: | Isaac Newton Institute | ||||
| Copyright: | Huang, H | ||||
| Language: | eng (English) | ||||
| Credits: |
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| Abstract: | Predicting phenotypes from genotypes is one of the major challenges of functional genomics. In this talk, we aim to take the first step into using microarray repositories to create a disease diagnosis database, or in general, for phenotype prediction. This will provide an important application for the enormous amount of costly generated, yet freely available, genomics data. In many disease diagnosis cases, it is not obvious which potential disease should be targeted, and screening across the enormous accumulation of disease expression profiles will help to narrow down the disease candidates. In addition, such profile-based-diagnosis is especially useful for those diseases that lack biochemical diagnosis tests. |
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