Published Articles >> Table of Contents >> Abstract

IEEE Computer Society Bioinformatics Conference (CSB'03)   p. 104
Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks
Seiya Imoto, University of Tokyo
Tomoyuki Higuchi, The Institute of Statistical Mathematics
Takao Goto, University of Tokyo
Kousuke Tashiro, Kyushu University
Satoru Kuhara, Kyushu University
Satoru Miyano, University of Tokyo

Full Article Text:

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSB.2003.1227309
Send link to a friend

Abstract
We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Unfortunately, microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.
Back to Top Additional Information
Citation:  Seiya Imoto, Tomoyuki Higuchi, Takao Goto, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano, "Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks," csb, p. 104,  IEEE Computer Society Bioinformatics Conference (CSB'03),  2003