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Advances in Bioinformatics
Volume 2013, Article ID 953814, 12 pages
http://dx.doi.org/10.1155/2013/953814
Review Article

An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks

1Electrical and Computer Engineering Department, Texas A&M University, College Station, TX 77843-3128, USA
2Chemical Engineering Department, Texas A&M University at Qatar, 253 Texas A&M Engineering Building, Education City, P.O. Box 23874, Doha, Qatar
3Electrical Engineering Department, Texas A&M University at Qatar, 253 Texas A&M Engineering Building, Education City, P.O. Box 23874, Doha, Qatar
4Department of Genetic Medicine, Weill Cornell Medical College in Qatar, P.O. Box 24144, Doha, Qatar

Received 20 July 2012; Revised 12 January 2013; Accepted 17 January 2013

Academic Editor: Yufei Huang

Copyright © 2013 Amina Noor et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.