{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:22:14Z","timestamp":1777983734205,"version":"3.51.4"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":162,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union Next Generation EU\/PRTR","award":["TED2021-129221B-I00"],"award-info":[{"award-number":["TED2021-129221B-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Exploring and categorizing essential and synthetic lethality genes is crucial in developing effective and targeted therapies for various diseases. This endeavor hinges upon genetic minimal cut sets, which also find utility in metabolic engineering. Different methods have been suggested for calculating genetic minimal cut sets. Still, with the emergence of numerous new models and their increasing complexity, it has become essential to introduce new algorithms in this field. This paper presents a new algorithmic approach for computing genetic minimal cut sets, which utilizes linear programming techniques to improve temporal efficiency. The key concept of the method is to use a k-representative subset to replace the target set with a smaller, yet representative, one. We have analyzed its efficiency in terms of running times compared to gMCSPy, the most recent published research on computing genetic minimal cut sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Software and additional material are freely available at https:\/\/github.com\/biogacop\/fastMethod<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioadv\/vbaf127","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T07:49:26Z","timestamp":1748591366000},"source":"Crossref","is-referenced-by-count":0,"title":["A fast method for extracting essential and synthetic lethality genes in GEM models"],"prefix":"10.1093","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6846-1888","authenticated-orcid":false,"given":"Francisco","family":"Guil","sequence":"first","affiliation":[{"name":"Parallel Computer Architecture Group, University of Murcia, CEIR Campus Mare Nostrum , Murcia 30100,","place":["Spain"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6388-2835","authenticated-orcid":false,"given":"Jos\u00e9 M","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Parallel Computer Architecture Group, University of Murcia, CEIR Campus Mare Nostrum , Murcia 30100,","place":["Spain"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"2025070916261271500_vbaf127-B1","author":"Banerjee","year":"2020"},{"key":"2025070916261271500_vbaf127-B2","volume-title":"Hypergraphs: combinatorics of Finite Sets","author":"Berge","year":"1984"},{"key":"2025070916261271500_vbaf127-B3","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1186\/s12859-019-2946-7","article-title":"Determination of growth-coupling strategies and their underlying 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