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Activity Number:
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146
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Type:
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Invited
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #302814 |
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Title:
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Identification of Cancer-Associated Gene Pathways from Analysis of Expression Data
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Author(s):
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Shuangge Ma*+ and Michael Kosorok
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Companies:
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Yale University and The University of North Carolina at Chapel Hill
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Address:
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, , CT, 06510,
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Keywords:
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large p, small n ; pathway ; penalization
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Abstract:
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Analysis of cancer genomic data can be challenging because of the high dimensionality and pathway structure of the covariates. With cancer gene expression data and pathway structure defined a priori, we first use dimension reduction methods to define a small number of representative features for each pathway. Penalization is then used to identify gene pathways and representative features within selected pathways that are associated with cancer clinical outcomes in the joint modeling of multiple gene pathways. We investigate consistency of gene pathway selection. Analysis of multiple cancer studies suggests that the proposed approach can identify biologically important gene pathways missed by using alternative approaches.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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