{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:22:13Z","timestamp":1753888933797,"version":"3.41.2"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":238,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006378","name":"Universitas Indonesia","doi-asserted-by":"publisher","award":["NKB-661\/UN2-RST\/HKP.05.00\/2022"],"award-info":[{"award-number":["NKB-661\/UN2-RST\/HKP.05.00\/2022"]}],"id":[{"id":"10.13039\/501100006378","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>Coronaviruses, including severe acute respiratory syndrome coronavirus 2 (SARS\u2010CoV\u20102), continue to pose a significant public health challenge globally, even in 2024. Despite advancements in vaccines and treatments, the accurate classification of coronavirus protein sequences remains crucial for monitoring variants, understanding viral behavior, and developing targeted interventions. In this study, we investigate the efficacy of various classification methods in accurately classifying coronavirus protein sequences. We explore the use of <jats:italic>K<\/jats:italic>\u2010nearest neighbor (KNN), fuzzy KNN (FKNN), support vector machine (SVM), and SVM with particle swarm optimization (PSO\u2010SVM) algorithms for classification, complemented by feature selection techniques including principal component analysis (PCA) and random forest\u2010recursive feature elimination (RF\u2010RFE). Our dataset comprises 2000 protein sequences, evenly split between SARS\u2010CoV\u20102 and non\u2010SARS\u2010CoV\u20102 sequences. Through rigorous analysis, we evaluate the performance of each classification model in terms of accuracy, sensitivity, specificity, and receiver operating characteristic area under the curve (ROC\u2010AUC). Our findings demonstrate consistently high performance across all models, reflecting their efficacy in classifying coronavirus protein sequences. Notably, the PCA\u2009+\u2009PSO\u2010SVM model emerges as the top\u2010performing model, exhibiting the highest classification accuracy, specificity, and ROC\u2010AUC score, demonstrating its effectiveness in distinguishing between SARS\u2010CoV\u20102 and non\u2010SARS\u2010CoV\u20102 sequences. Overall, our study highlights the importance of employing advanced classification methods and feature selection techniques in accurately classifying coronavirus protein sequences. The findings provide valuable insights for researchers and practitioners in the field of bioinformatics and contribute to ongoing efforts in understanding and combating the COVID\u201019 pandemic and its evolving challenges.<\/jats:p>","DOI":"10.1155\/2024\/8683822","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T06:34:27Z","timestamp":1724740467000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Protein Sequence\u2010Based COVID\u201019 Detection: A Comparative Study of Machine Learning Classification Methods"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9650-7540","authenticated-orcid":false,"given":"Siti","family":"Aminah","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9999-7896","authenticated-orcid":false,"given":"Gianinna","family":"Ardaneswari","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9715-5161","authenticated-orcid":false,"given":"Mohd Khalid","family":"Awang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5154-3109","authenticated-orcid":false,"given":"Muhammad Ariq","family":"Yusaputra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8797-241X","authenticated-orcid":false,"given":"Dian Puspita","family":"Sari","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-29024-x"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40998-022-00569-3"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-021-00807-7"},{"key":"e_1_2_8_4_2","article-title":"Particle swarm optimization","author":"Kennedy J.","year":"1995","journal-title":"IEEE Xplore"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/546\/5\/052077"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2020.105.116"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbrc.2020.09.010"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/nbt0308-303"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1968\/1\/012016"},{"key":"e_1_2_8_10_2","first-page":"60","article-title":"Multivariate statistical data analysis-principal component analysis (PCA)","volume":"7","author":"Mishra S. 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