The Max-Min High-Order Dynamic Bayesian Network for Learning Gene Regulatory Networks with Time-Delayed Regulations

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Yifeng LiAlioune Ngom

Abstract

Accurately reconstructing gene regulatory network (GRN) from gene expression data is a challenging task in systems biology. Although some progresses have been made, the performance of GRN reconstruction still has much room for improvement. Because many regulatory events are asynchronous, learning gene interactions with multiple time delays is an effective way to improve the accuracy of GRN reconstruction. Here, we propose a new approach, called Max-Min high-order dynamic Bayesian network (MMHO-DBN) by extending the Max-Min hill-climbing Bayesian network technique originally devised for learning a Bayesian network's structure from static data. Our MMHO-DBN can explicitly model the time lags between regulators and targets in an efficient manner. It first uses constraint-based ideas to limit the space of potential structures, and then applies search-and-score ideas to search for an optimal HO-DBN structure. The performance of MMHO-DBN to GRN reconstruction was evaluated using both synthetic and real gene expression time-series data. Results show that MMHO-DBN is more accurate than current time-delayed GRN learning methods, and has an intermediate computing performance. Furthermore, it is able to learn long time-delayed relationshi...Continue Reading

References

Dec 7, 2000·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·N FriedmanD Pe'er
Apr 27, 2005·Journal of Bioinformatics and Computational Biology·Chris Ding, Hanchuan Peng
Nov 19, 2008·Nature Reviews. Genetics·Zhong WangMichael Snyder
May 24, 2013·Briefings in Bioinformatics·Yongsoo KimDaehee Hwang

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