Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

@article{Tamada2011EstimatingGG,
  title={Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers},
  author={Yoshinori Tamada and Seiya Imoto and Hiromitsu Araki and Masao Nagasaki and Cristin G. Print and Stephen D. Charnock-Jones and Satoru Miyano},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  year={2011},
  volume={8},
  pages={683-697}
}
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… CONTINUE READING
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