Fusion of Gene Regulatory and Protein Interaction Networks Using Skip-Chain Models
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Building gene networks with time-delayed regulations
Pattern Recognition Letters
Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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Most of the current applications which use dynamic Bayesian network to model gene regulatory network assume that the time delay between regulators and their targets is one time unit in a time series gene expression dataset. In fact, multiple time units delay is indicated to exist in a gene regulation process. In this paper, we propose using higherorder Markov dynamic Bayesian network(DBN) to model multiple time units delayed gene regulatory network. A two steps heuristic learning framework is designed to learn higher-order Markov DBN from time series gene expression data. We apply the learning framework to a yeast cell cycle gene expression dataset. The predicted gene regulatory network is strongly supported by biological evidence and consistent with the yeast cell cycle phase information.