J Biomed Inform - SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
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Resumo

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.

Resumo Limpo

bayesian network use infer transcript regulatori relationship among gene valuabl obtain biolog insight howev find optim bayesian network bn nphard thus heurist approach sought effect solv problem work develop hybrid search method combin simul anneal greedi algorithm saga saga explor search space undergo twophas search first simul anneal search greedi search three set backgroundcorrect normal microarray dataset use test algorithm bn structur learn also conduct use dataset establish search method implement banjo bayesian network infer java object bayesian dirichlet equival bde metric use score network produc saga saga predict transcript regulatori relationship among gene network evalu higher bde score high sensit specif thus propos method compet well exist search algorithm bayesian network structur learn transcript regulatori network

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