Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers
Issue No. 03 - May/June (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.68
Yoshinori Tamada , University of Tokyo, Tokyo
Seiya Imoto , University of Tokyo, Tokyo
Hiromitsu Araki , Cell Innovator, Inc
Masao Nagasaki , University of Tokyo, Tokyo
Cristin Print , University of Auckland, Auckland
D. Stephen Charnock-Jones , University of Cambridge, Cambridge
Satoru Miyano , University of Tokyo, Tokyo
We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.
Biology and genetics, gene networks, Bayesian network structure learning, gene expression data analysis.
C. Print et al., "Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 683-697, 2010.