{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T15:41:43Z","timestamp":1772034103291,"version":"3.50.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability of NGS datasets have led to a significant surge in biomedical research. To better understand the molecular processes, underlying cancer and to support its development, diagnosis, prediction, and therapy; NGS data analysis is crucial. However, the NGS multi-layer omics high-dimensional dataset is highly complex. In recent times, some computational methods have been developed for cancer omics data interpretation. However, various existing methods face challenges in accounting for diverse types of cancer omics data and struggle to effectively extract informative features for the integrated identification of core units. To address these challenges, we proposed a hybrid feature selection (HFS) technique to detect optimal features from multi-layer omics datasets. Subsequently, this study proposes a novel hybrid deep recurrent neural network-based model DOMSCNet to classify stomach cancer. The proposed model was made generic for all four multi-layer omics datasets. To observe the robustness of the DOMSCNet model, the proposed model was validated with eight external datasets. Experimental results showed that the SelectKBest-maximum relevancy minimum redundancy-Boruta (SMB), HFS technique outperformed all other HFS techniques. Across four multi-layer omics datasets and validated datasets, the proposed DOMSCNet model outdid existing classifiers along with other proposed classifiers.<\/jats:p>","DOI":"10.1093\/bib\/bbaf115","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T11:56:26Z","timestamp":1743681386000},"source":"Crossref","is-referenced-by-count":6,"title":["DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0818-4419","authenticated-orcid":false,"given":"Kasmika","family":"Borah","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Technology, Cotton University, Hem Baruah Rd, Panbazar , Guwahati, Kamrup Metropolitan district, Assam 781001 ,","place":["India"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4112-5566","authenticated-orcid":false,"given":"Himanish Shekhar","family":"Das","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Cotton University, Hem Baruah Rd, Panbazar , Guwahati, Kamrup Metropolitan district, Assam 781001 ,","place":["India"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7716-9897","authenticated-orcid":false,"given":"Ram Kaji","family":"Budhathoki","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, School of Engineering, Kathmandu University , Kavrepalanchok district, Dhulikhel 45200 ,","place":["Nepal"]}]},{"given":"Khursheed","family":"Aurangzeb","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University , P. 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Chan School of Public Health , 665 Huntington Avenue, Boston, MA 02115 ,","place":["United States"]},{"name":"Department of Pharmacology & Toxicology, University of Arizona , 1295 N Martin Ave, Pima district, Tucson, AZ 85721 ,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"2025050203370347600_ref1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.14348\/molcells.2021.0042","article-title":"Integrative multi-omics approaches in cancer research: from biological networks to clinical subtypes","volume":"44","author":"Heo","year":"2021","journal-title":"Mol Cells"},{"key":"2025050203370347600_ref2","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13748-015-0080-y","article-title":"Feature selection for high-dimensional data","volume":"5","author":"Bol\u00f3n-Canedo","year":"2016","journal-title":"Prog Artif 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