NDLI: Integrating epigenetic prior in dynamic Bayesian network for gene regulatory network inference

Content Provider	IEEE Xplore Digital Library
Author	Haifen Chen Maduranga, D.A.K. Mundra, P.A. Jie Zheng
Copyright Year	2013
Description	Author affiliation: Bioinf. Res. Centre, Nanyang Technol. Univ., Singapore, Singapore (Haifen Chen; Maduranga, D.A.K.; Mundra, P.A.; Jie Zheng)
Abstract	Gene regulatory network (GRN) inference from high throughput biological data has drawn a lot of research interest in the last decade. However, due to the complexity of gene regulation and lack of sufficient data, GRN inference still has much space to improve. One way to improve the inference of GRN is by developing methods to accurately combine various types of data. Here we apply dynamic Bayesian network (DBN) to infer GRN from time-series gene expression data where the Bayesian prior is derived from epigenetic data of histone modifications. We propose several kinds of prior from histone modification data, and use both real and synthetic data to compare their performance. Parameters of prior integration are also studied to achieve better results. Experiments on gene expression data of yeast cell cycle show that our methods increase the accuracy of GRN inference significantly.
Sponsorship	IEEE Comput. Intelligence Soc.
Starting Page	76
Ending Page	82
File Size	426162
Page Count	7
File Format	PDF
ISBN	9781467358750
DOI	10.1109/CIBCB.2013.6595391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayes methods Correlation Gene expression Mathematical model Bioinformatics Vectors Biological system modeling yeast cell cycle Gene regulatory network dynamic Bayesian network epigenetics histone modification gene expression
Content Type	Text
Resource Type	Article

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