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Activity Number:
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473
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304780 |
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Title:
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A Novel Approach to Learning Gene Association Networks from High-Dimensional Data
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Author(s):
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Jie Cheng*+ and Xiwu Lin and Kwan R. Lee
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Companies:
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GlaxoSmithKline and GlaxoSmithKline and GlaxoSmithKline
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Address:
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1250 S Collegeville Road, Collegeville, PA, 19426,
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Keywords:
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gene association networks ; graphical models ; graphical Gaussian models ; Bayesian networks ; Markov networks ; estimating partial correlation matrix
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Abstract:
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Inferring large scale gene networks from limited continuous data is a challenging problem in bioinformatics. This problem is closely related to partial correlation matrix estimation and graphical Gaussian model learning. We developed a java based tool for such task based on our previous work on learning Bayesian network from multinomial data. Given a data sets or a covariance matrix as input, the tool can efficiently construct either a directed or an undirected network. Results based on simulation data sets show that the tool compares favorable to the popular R package GeneNet in term of accuracy. Results based on public functional genomics data sets are also given.
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