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Studies on common methods in inference of gene regulatory networks are yet to pay attention to their biological importance and as such, predictions by these methods are less accurate in terms of biological significance. Hence, in the proposed method, genes with high correlation were identified in one cluster using clustering, and the existence of edge between the genes in the cluster was prevented. Finally, after the Bayesian network modeling, based on knowledge gained from clustering, the refining phase and improving regulatory interactions using biological correlation were done. In order to show the efficiency, the proposed method has been compared with several common methods in this area including GENIE3 and BMALR. The results of the evaluation indicate that the proposed method recognized regulatory relations in Bayesian modeling process well, due to using of biological knowledge which is hidden in the data collection, and is able to recognize gene regulatory networks align with important methods in this field.<\/jats:p>","DOI":"10.1142\/s0219720019500185","type":"journal-article","created":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T09:33:26Z","timestamp":1554888806000},"page":"1950018","source":"Crossref","is-referenced-by-count":8,"title":["TIGRNCRN: Trustful inference of gene regulatory network using clustering and refining the network"],"prefix":"10.1142","volume":"17","author":[{"given":"Jamshid","family":"Pirgazi","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Engineering Faculty, University of Zanjan, Zanjan, Iran"}]},{"given":"Ali Reza","family":"Khanteymoori","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, 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