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A Linear Programming Framework for Inferring Gene Regulatory Networks by Integrating Heterogeneous Data
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Author(s): Yong Wang (Academy of Mathematics and Systems Science, China), Rui-Sheng Wang (Renmin University, China), Trupti Joshi (University of Missouri, USA), Dong Xu (University of Missouri, USA), Xiang-Sun Zhang (Academy of Mathematics and Systems Science, China)and Luonan Chen (Osaka Sangyo University, Japan)
Copyright: 2010
Pages: 26
Source title:
Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Source Author(s)/Editor(s): Sanjoy Das (Kansas State University, USA), Doina Caragea (Kansas State University, USA), Stephen Welch (Kansas State University, USA)and William H. Hsu (Kansas State University, USA)
DOI: 10.4018/978-1-60566-685-3.ch019
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Abstract
There exist many heterogeneous data sources that are closely related to gene regulatory networks. These data sources provide rich information for depicting complex biological processes at different levels and from different aspects. Here, we introduce a linear programming framework to infer the gene regulatory networks. Within this framework, we extensively integrate the available information derived from multiple time-course expression datasets, ChIP-chip data, regulatory motif-binding patterns, protein-protein interaction data, protein-small molecule interaction data, and documented regulatory relationships in literature and databases. Results on synthetic and real experimental data both demonstrate that the linear programming framework allows us to recover gene regulations in a more robust and reliable manner.
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