title: SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.
abstract: Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.
authors.affiliation: Department of Mathematics, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana. Electronic address: healmes@gmail.com.
authors.forename: Emmanuel S
authors.initials: ES
authors.lastname: Adabor
authors.affiliation: Pharmaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences (MCPHS University), 19 Foster Street, Worcester, MA, USA.
authors.forename: George K
authors.initials: GK
authors.lastname: Acquaah-Mensah
authors.affiliation: Department of Mathematics, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana.
authors.forename: Francis T
authors.initials: FT
authors.lastname: Oduro
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000378:metabolism; D001943:Breast Neoplasms / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D051379:Mice; D015233:Models, Statistical; D009130:Muscle, Smooth / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014132:Trachea / Q000378:metabolism
keywords: Bayesian Network; Inference; Microarray dataset; Search algorithms; Transcriptional regulatory network