NDLI: A Markov-Blanket-Based Model for Gene Regulatory Network Inference

Content Provider	ACM Digital Library
Author	Chetty, Madhu Ram, Ramesh
Abstract	An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.
Starting Page	353
Ending Page	367
Page Count	15
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2009.70
Volume Number	8
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cause-effect analysis, causal modeling, gene regulatory network, genetic algorithms, microarray gene expression data, network inference.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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