{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T21:57:44Z","timestamp":1769291864969,"version":"3.49.0"},"reference-count":21,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>High\u2010dimensional gene expression datasets pose significant challenges for cancer classification due to the presence of redundant and irrelevant features. To address this issue, we propose a hybrid framework that integrates the flower pollination algorithm (FPA) with support vector machines (SVM) for effective feature selection and classification. The FPA, inspired by the global and local pollination processes of flowering plants, is adapted into a binary variant using a sigmoid transfer function to select informative subsets of genes. The objective function balances classification accuracy with feature subset sparsity, thereby reducing dimensionality while preserving discriminative power. The selected gene subsets are subsequently evaluated using SVM, which provides robust classification in small\u2010sample, high\u2010dimensional scenarios. The proposed FPA\u2010SVM framework was tested on multiple benchmark cancer datasets, including colon tumor, CNS, ALL\u2010AML, breast cancer, lung cancer, ovarian cancer, lymphoma, MLL, and SRBCT. Experimental results demonstrate superior performance, with accuracy levels exceeding 98% for most binary\u2010class datasets and competitive results for multiclass datasets, achieving up to 88.3% accuracy. These findings highlight the effectiveness of the proposed method in enhancing cancer classification, reducing dimensionality, and identifying potential biomarkers for precision medicine.<\/jats:p>","DOI":"10.1002\/cpe.70562","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:06:12Z","timestamp":1768878372000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Nature\u2010Inspired Framework for Dimensionality Reduction and Cancer Diagnosis From Gene Expression Profiles"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1426-0864","authenticated-orcid":false,"given":"Abrar","family":"Yaqoob","sequence":"first","affiliation":[{"name":"Department of Mathematics VIT Bhopal University Located at Kothrikalan  Bhopal India"}]},{"given":"Khawaja T.","family":"Tasneem","sequence":"additional","affiliation":[{"name":"College of Computing and Informatics Saudi Electronic University  Riyadh Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4388-1165","authenticated-orcid":false,"given":"Mushtaq Ahmad","family":"Mir","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory Sciences, College of Applied Medical Sciences King Khalid University  Abha Saudi Arabia"}]},{"given":"R. Vijaya","family":"Lakshmi","sequence":"additional","affiliation":[{"name":"Faculty of Management Studies ICFAI University  Raipur Chhattisgarh India"}]},{"given":"Tejaswini","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Department of Mathematics Kalinga University  Chhattisgarh India"}]},{"given":"G. V. V. Jagannadha","family":"Rao","sequence":"additional","affiliation":[{"name":"Department of Mathematics Kalinga University  Chhattisgarh India"},{"name":"Department of Mathematics, Faculty of Science ICFAI University  Raipur Chhattisgarh India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2821-5423","authenticated-orcid":false,"given":"Mohd Asif","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Bakhtar University  Kabul Afghanistan"},{"name":"Division of Research and Development Lovely Professional University  Phagwara Punjab India"},{"name":"University Centre for Research &amp; Development Chandigarh University  Mohali Punjab India"}]}],"member":"311","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.4108\/eai.19\u201012\u20102018.156086"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.531"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489\u2010022\u201003971\u2010w"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2005.09.024"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010022\u201014052\u2010y"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.14311\/NNW.2023.33.005"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010022\u201007780\u20107"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2023.100245"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2997311"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00366\u2010011\u20100241\u2010y"},{"key":"e_1_2_14_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.02.028"},{"key":"e_1_2_14_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/math10030464"},{"key":"e_1_2_14_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108912"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.21275\/ART20203995"},{"key":"e_1_2_14_16_1","volume-title":"2014 Int. 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