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Inferring Genetic Networks from Microarray Data
Stanford, California August 16-August 19
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSB.2004.13324982004 IEEE Computational Systems Bioin ...
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Shawn Martin, Sandia National Laboratories
George Davidson, Sandia National Laboratories
Elebeoba May, Sandia National Laboratories
Jean-Loup Faulon, Sandia National Laboratories
Margaret Werner-Washburne, University of New Mexico
In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are 1) inferring the network; 2) estimating the stability of the inferred network; and 3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns.
Citation:
Shawn Martin, George Davidson, Elebeoba May, Jean-Loup Faulon, Margaret Werner-Washburne, "Inferring Genetic Networks from Microarray Data," csb, pp.566-569, 2004 IEEE Computational Systems Bioinformatics Conference (CSB'04), 2004
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