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Gene expression and metadata based identification of key genes for lung cancer, COPD, and IPF using machine learning and statistical models.

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Title: Gene expression and metadata based identification of key genes for lung cancer, COPD, and IPF using machine learning and statistical models.
Authors: Yasmin MF; Department of Computing and Information System(CIS), Daffodil International University (DIU), Ashulia, Dhaka, Bangladesh., Hosen MF; Department of Computing and Information System(CIS), Daffodil International University (DIU), Ashulia, Dhaka, Bangladesh.; Department of Computer Science and Engineering, Netrokona University, Netrokona, Bangladesh., Basar MA; Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka, Bangladesh., Rahman A; Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka, Bangladesh.; School of Computing, Georgia Southern University, Statesboro, Georgia, United States of America., Hasan M; Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University (MBSTU), Santosh, Tangail, Bangladesh., Al Farid F; Faculty of Computer Science and Informatics, Berlin School of Business and Innovation Karl-Marx-Straße 97-99, Berlin, Germany.; Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya, Malaysia., Karim HA; Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya, Malaysia., Miah ASM; Department of Computer Science and Engineering, Rajshahi University, Rajshahi, Bangladesh.; Graduate Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan.
Source: PloS one [PLoS One] 2026 Mar 19; Vol. 21 (3), pp. e0344666. Date of Electronic Publication: 2026 Mar 19 (Print Publication: 2026).
Publication Type: Journal Article
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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ISSN:1932-6203
DOI:10.1371/journal.pone.0344666