- Title
- A Markov-blanket-based model for gene regulatory network inference
- Creator
- Ram, Ramesh; Chetty, Madhu
- Date
- 2011
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/64804
- Identifier
- vital:5958
- Identifier
-
https://doi.org/10.1109/TCBB.2009.70
- Identifier
- ISSN:1545-5963
- 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.
- Relation
- Transactions on Computational Biology and Bioinformatics Vol. 8, no. 2 (2011), p.
- Rights
- Copyright Institute of Electrical and Electronics Engineers
- Rights
- This metadata is freely available under a CCO license
- Subject
- 01 Mathematical Sciences; 06 Biological Sciences; 08 Information and Computing Sciences
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