Journal
JOURNAL OF THEORETICAL BIOLOGY
Volume 362, Issue -, Pages 53-61Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2014.03.040
Keywords
Coexpression networks; Bayesian networks; Dynamic networks; Community detection; Genomic data integration
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Funding
- NIH [U01HG007031]
- NSF [DMS-1160319]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1026441] Funding Source: National Science Foundation
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Network modeling has proven to be a fundamental tool in analyzing the inner workings of a cell. It has revolutionized our understanding of biological processes and made significant contributions to the discovery of disease biomarkers. Much effort has been devoted to reconstruct various types of biochemical networks using functional genomic datasets generated by high-throughput technologies. This paper discusses statistical methods used to reconstruct gene regulatory networks using gene expression data. In particular, we highlight progress made and challenges yet to be met in the problems involved in estimating gene interactions, inferring causality and modeling temporal changes of regulation behaviors. As rapid advances in technologies have made available diverse, large-scale genomic data, we also survey methods of incorporating all these additional data to achieve better, more accurate inference of gene networks. (C) 2014 Elsevier Ltd. All rights reserved.
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