{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T15:38:43Z","timestamp":1649086723257},"reference-count":47,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","funder":[{"name":"the General Program of NSFC","award":["61773300"],"award-info":[{"award-number":["61773300"]}]},{"name":"the Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China","award":["2017JZ017"],"award-info":[{"award-number":["2017JZ017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> Inferring gene regulatory networks (GRNs) is vital to understand the complex cellular processes and reveal the regulatory mechanisms among genes. Although various methods have been developed, more accurate algorithms which can control the sparseness of GRNs still need to be developed. In this work, we model GRNs by fuzzy cognitive maps (FCMs), and a node in an FCM means a gene. Then, a new sparse and decomposed particle swarm optimization, termed as SDPSO<jats:sub>FCM<\/jats:sub>-GRN, is proposed to train FCMs, which employs the least absolute shrinkage and selection operator (Lasso) to control the network sparseness with a decomposed strategy. In the experiments, the performance of SDPSO<jats:sub>FCM<\/jats:sub>-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that SDPSO<jats:sub>FCM<\/jats:sub>-GRN can well control the sparseness of GRNs, and infer directed GRNs with high accuracy and efficiency. <\/jats:p>","DOI":"10.1142\/s0219720019500239","type":"journal-article","created":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T05:44:12Z","timestamp":1557467052000},"page":"1950023","source":"Crossref","is-referenced-by-count":1,"title":["A sparse and decomposed particle swarm optimization for inferring gene regulatory networks based on fuzzy cognitive maps"],"prefix":"10.1142","volume":"17","author":[{"given":"Luowen","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, P.\u00a0R.\u00a0China"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 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