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Activity Number:
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13
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #303634 |
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Title:
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Statistical Methods for Analysis of Genomic Data with Graphic Structure
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Author(s):
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Caiyan Li*+
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Companies:
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University of Pennsylvania
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Address:
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503 Blockley Hall, Biostatistics and Epidemiology Department, Philadelphia, PA, 19104,
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Keywords:
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Graphs ; Networks ; regularization ; Genomic data
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Abstract:
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Graphs and networks are common ways of depicting information. In biology, many different biological processes are represented by graphs, such as regulatory networks, metabolic pathways and protein-protein interaction networks. This kind of a priori information is a useful supplement to the numerical genomic data. How to incorporate information encoded by the known biological networks or graphs into analysis of numerical data raises interesting statistical challenges. In this talk, I will present a network-constrained regularization procedure to identify sub-networks that are predictive of a certain clinical outcome. I will present the formulation of the problem and some theoretical results. Simulation studies indicated that the method is quite effective in identifying genes that are related to disease. I will demonstrate the proposed method by studying a microarray gene expression data.
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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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