Identification of key candidate genes for ovarian cancer using integrated statistical and machine learning approaches.

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Bibliographic Details
Title: Identification of key candidate genes for ovarian cancer using integrated statistical and machine learning approaches.
Authors: Hossain MA; NanoBio Technology Center, and Computer Science and Engineering, Daffodil International University, Birulia, Savar, Dhaka-1216, Bangladesh.; Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh., Asa TA; NanoBio Technology Center, and Computer Science and Engineering, Daffodil International University, Birulia, Savar, Dhaka-1216, Bangladesh.; Computer Science and Engineering, Jagannath University, 9-10 Chittaranjan Avenue, Sadarghat, Dhaka-1100, Bangladesh., Islam MS; Institute for Intelligent Systems Research and Innovation (ISSRI), Deakin University, 75 Pigdons Road, 3216 Warun Ponds, Victoria, Australia., Rahman MZ; Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh., Moni MA; Health Sciences Research Center (HSRC), Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.; AI and Digital Health Technology, Rural Health Research Institute, Charles Sturt University, Orange, 2800 NSW, Australia.; AI and Digital Health Technology, AI and Cyber Futures Institute, Charles Sturt University, Bathurst, 2795 NSW, Australia.
Source: Briefings in bioinformatics [Brief Bioinform] 2025 Nov 01; Vol. 26 (6).
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1477-4054
DOI:10.1093/bib/bbaf602