A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.

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Title: A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.
Authors: Al-Rajab M; College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom., Lu J; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom., Xu Q; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom., Kentour M; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom., Sawsa A; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.; Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom., Shuweikeh E; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom., Joy M; University of Warwick, Coventry, United Kingdom., Arasaradnam R; University Hospital Coventry & Warwickshire, Coventry, United Kingdom.
Source: PloS one [PLoS One] 2023 Nov 02; Vol. 18 (11), pp. e0286791. Date of Electronic Publication: 2023 Nov 02 (Print Publication: 2023).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Review
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.
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  Data: <searchLink fieldCode="AU" term="%22Al-Rajab+M%22">Al-Rajab M</searchLink>; College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Lu+J%22">Lu J</searchLink>; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Xu+Q%22">Xu Q</searchLink>; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Kentour+M%22">Kentour M</searchLink>; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Sawsa+A%22">Sawsa A</searchLink>; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.; Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Shuweikeh+E%22">Shuweikeh E</searchLink>; School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Joy+M%22">Joy M</searchLink>; University of Warwick, Coventry, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Arasaradnam+R%22">Arasaradnam R</searchLink>; University Hospital Coventry & Warwickshire, Coventry, United Kingdom.
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  Data: <searchLink fieldCode="JN" term="%22101285081%22">PloS one</searchLink> [PLoS One] 2023 Nov 02; Vol. 18 (11), pp. e0286791. <i>Date of Electronic Publication: </i>2023 Nov 02 (<i>Print Publication: </i>2023).
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        Value: 10.1371/journal.pone.0286791
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