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Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks (2003)
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| Venue: | In Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB 03 |
| Citations: | 80 - 6 self |
BibTeX
@INPROCEEDINGS{Imoto03combiningmicroarrays,
author = {Seiya Imoto and Tomoyuki Higuchi and Takao Goto and Kousuke Tashiro and Satoru Kuhara and Satoru Miyano},
title = {Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks},
booktitle = {In Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB 03},
year = {2003},
pages = {104--113},
publisher = {IEEE}
}
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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. 1.
Keyphrases
biological knowledge gene network bayesian network many case microarray information saccharomyces cerevisiae gene expression data microarray data bayesian statistical framework site information protein-protein interaction monte carlo simulation statistical method enough information protein-dna interaction microarray gene expression data estimation method
