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Activity Number:
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178
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 4, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #301464 |
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Title:
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Statistical Methods for Analysis of Genomic Data with Graphical Structures
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Author(s):
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Caiyan Li*+ and Hongzhe Li+
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Companies:
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University of Pennsylvania and University of Pennsylvania
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Address:
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Department of Biostatistics & Epidemiology, Philadelphia, PA, 19104-6021, Department of Biostatistics and Epidemiology , Philadelphia, PA, 19104,
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Keywords:
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Gene expression ; Network ; eQTL ; Markov random field ; Regularization
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Abstract:
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Graphs and networks are common ways of depicting information. In biology in particular, many different biological processes, such as regulatory networks, metabolic pathways, and protein-protein interaction networks, are represented by graphs. This kind of a priori information gathered over many years of biomedical research is a useful supplement to the standard numerical genomic data such as microarray gene expression data. However, how to efficiently incorporate information encoded by the known biological networks represented as graphs into analysis of various types of numerical genomic data raises interesting statistical and computational challenges. We present several new statistical methods for incorporating network information in to analysis of genomic data, including a network-constrained regularization procedure and a hidden Markov random field approach for analysis of eQTL 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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