Learning bayesian networks by lamarckian genetic algorithm and its application to yeast cell-cycle gene network reconstruction from time-series microarray data

Sun Chong Wang, Sai Ping Li

研究成果: 書貢獻/報告類型篇章同行評審

4 引文 斯高帕斯(Scopus)

摘要

A gene network depicts the inter-regulatory relations among genes. Knowledge of the gene network is key to an understanding of biological processes. A Bayesian network, consisting of nodes and directed arcs, is a convenient vehicle to model gene networks. We described a nonlinear model for the rate of gene transcription. Levels of gene expression are continuous in the model. We employed a genetic algorithm to evolve the structure of a Bayesian network. Given a candidate structure, the best parameters are estimated by the downhill simplex algorithm. The methodology features a reconstruction resolution that is limited by data noise. We tested the implementation by artificial gene networks in simulations. We then applied the methodology to reconstruct the regulation network of 27 yeast cell cycle genes from a real microarray dataset. The result obtained is promising: 17 out of the 22 reconstructed regulations are consistent with experimental findings.

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主出版物標題Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
編輯Auke Jan Ijspeert, Masayuki Murata, Naoki Wakamiya
發行者Springer Verlag
頁面49-62
頁數14
ISBN(列印)3540233393, 9783540233398
DOIs
出版狀態已出版 - 2004

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3141
ISSN(列印)0302-9743
ISSN(電子)1611-3349

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