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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.<\/jats:p>","DOI":"10.1155\/2013\/953814","type":"journal-article","created":{"date-parts":[[2013,2,21]],"date-time":"2013-02-21T23:10:40Z","timestamp":1361488240000},"page":"1-12","source":"Crossref","is-referenced-by-count":18,"title":["An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks"],"prefix":"10.1155","volume":"2013","author":[{"given":"Amina","family":"Noor","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, Texas A&M University, College Station, TX 77843-3128, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erchin","family":"Serpedin","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Texas A&M University, College Station, TX 77843-3128, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Nounou","sequence":"additional","affiliation":[{"name":"Chemical Engineering Department, Texas A&M University at Qatar, 253 Texas A&M Engineering Building, Education City, P.O. 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