{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T09:05:54Z","timestamp":1765098354862},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2021 Research Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>It is a challenging problem in systems biology to infer both the network structure and dynamics of a gene regulatory network from steady-state gene expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient in inference of large-scale networks. Therefore, it is necessary to develop a method to infer the network structure and dynamics accurately on large-scale networks using steady-state expression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we propose a novel constrained genetic algorithm-based Boolean network inference (CGA-BNI) method where a Boolean canalyzing update rule scheme was employed to capture coarse-grained dynamics. Given steady-state gene expression data as an input, CGA-BNI identifies a set of path consistency-based constraints by comparing the gene expression level between the wild-type and the mutant experiments. It then searches Boolean networks which satisfy the constraints and induce attractors most similar to steady-state expressions. We devised a heuristic mutation operation for faster convergence and implemented a parallel evaluation routine for execution time reduction. Through extensive simulations on the artificial and the real gene expression datasets, CGA-BNI showed better performance than four other existing methods in terms of both structural and dynamics prediction accuracies. Taken together, CGA-BNI is a promising tool to predict both the structure and the dynamics of a gene regulatory network when a highest accuracy is needed at the cost of sacrificing the execution time.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Source code and data are freely available at https:\/\/github.com\/csclab\/CGA-BNI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab295","type":"journal-article","created":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T11:17:07Z","timestamp":1619263027000},"page":"i383-i391","source":"Crossref","is-referenced-by-count":23,"title":["A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data"],"prefix":"10.1093","volume":"37","author":[{"given":"Hung-Cuong","family":"Trinh","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Ton Duc Thang University , Ho Chi Minh 758307, Vietnam"}]},{"given":"Yung-Keun","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of IT Convergence, University of Ulsan , Ulsan 680-749, Korea"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410280444400_btab295-B1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.2202\/1544-6115.1727","article-title":"Fitting Boolean networks from steady state perturbation data","volume":"10","author":"Almudevar","year":"2011","journal-title":"Stat. 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