Abstract Details
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Activity Number:
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176
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #309581 |
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Title:
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Regularized Canonical Correlation and Application to High-Dimensional Biomarker Data with Survival Endpoint
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Author(s):
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Li Liu*+
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Companies:
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Sanofi
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Keywords:
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dimension reduction ;
regularized canonical correlation ;
biomarker data
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Abstract:
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As the omics technologies advance, there is a need to study multiple omics data from same or different platforms simultaneously and also link them with clinical outcomes. In this paper, we focus on linking gene expression data with survival outcomes.
We propose to use regularized canonical correlation combined with B-splines to identify the survival related genes and genetic pathways and then apply Cox proportional hazard model to predict the survival endpoint. We demonstrated the usefulness of our methods through simulations and also applied the method to the real micro array datasets.
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Authors who are presenting talks have a * after their name.
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