title: Variable neighborhood search for reverse engineering of gene regulatory networks.
abstract: A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.
authors.affiliation: School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, United States. Electronic address: cnicholson@ou.edu.
authors.forename: Charles
authors.initials: C
authors.lastname: Nicholson
authors.affiliation: School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, United States. Electronic address: leslie.goodwin@ou.edu.
authors.forename: Leslie
authors.initials: L
authors.lastname: Goodwin
authors.affiliation: Guidhall, Southern Methodist University, Dallas, TX, United States. Electronic address: coreyc@smu.edu.
authors.forename: Corey
authors.initials: C
authors.lastname: Clark
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans
keywords: Gene regulatory networks; Reverse engineering; Variable neighborhood search