{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:51:37Z","timestamp":1777697497043,"version":"3.51.4"},"reference-count":18,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2022,6,17]]},"abstract":"<jats:p>Biomarker plays an important role in early disease diagnosis including cancer. The World Health Organization defines a biomarker as any structure or process in the body that is measurable and affects the prognosis or outcome of the disease. Today, biomarkers can be identified using bioinformatics tools. The detection of biomarkers in the field of bioinformatics is considered more as a problem of feature selection. Many feature selection algorithms have been used for biomarker discovery however these algorithms do not have enough accuracy or have computational complexity. For this reason, the researchers discard the high accuracy algorithms because they are time consuming. We redesigned an efficient algorithm based on parallel algorithms. We used the Cancer Genome Atlas (TCGA) including breast cancer patients. The proposed algorithm has the same accuracy and increases the speed of algorithm.<\/jats:p>","DOI":"10.3233\/idt-210227","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T11:43:43Z","timestamp":1653047023000},"page":"441-447","source":"Crossref","is-referenced-by-count":0,"title":["A parallel feature selection algorithm for detection of cancer biomarkers"],"prefix":"10.1177","volume":"16","author":[{"given":"Maryam","family":"Razmjouei","sequence":"first","affiliation":[]},{"given":"Hamid Reza","family":"Hamidi","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDT-210227_ref1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21654","article-title":"Cancer Statistics","volume":"71","author":"Siegel","year":"2021","journal-title":"CA Cancer J Clin."},{"issue":"1","key":"10.3233\/IDT-210227_ref2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/1471-2105-13-71","article-title":"Cancer bioinformatics: A new approach to systems clinical medicine","volume":"2","author":"Wu","year":"2012","journal-title":"BMC Bioinformatics."},{"key":"10.3233\/IDT-210227_ref3","doi-asserted-by":"crossref","unstructured":"Moses H, Nass S. Cancer biomarkers: the promises and challenges of improving detection and treatment. National Academies Press. 2007.","DOI":"10.17226\/11892"},{"key":"10.3233\/IDT-210227_ref4","unstructured":"Joshi G, Kaur R, Kaur H. Biomarkers in cancer. Biology, Medicine. 2016."},{"key":"10.3233\/IDT-210227_ref5","doi-asserted-by":"crossref","unstructured":"Panagoulias DP, Sotiropoulos DN, Tsihrintzis GA. Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization. 12th International Conference on Information, Intelligence, Systems and Applications (IISA 2021). Chania, Crete, Greece. 2021.","DOI":"10.1109\/IISA52424.2021.9555512"},{"key":"10.3233\/IDT-210227_ref6","doi-asserted-by":"crossref","unstructured":"Liu H, Motoda H. Computational methods of feature selection. Chapman & Hall Crc, ISBN978-1-58488-878-9, 2007.","DOI":"10.1201\/9781584888796"},{"issue":"4","key":"10.3233\/IDT-210227_ref7","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1093\/bib\/bbs026","article-title":"Reverse engineering biomolecular systems using omic data: challenges, progress and opportunities","volume":"13","author":"Quo","year":"2012","journal-title":"Briefings Bioinform."},{"issue":"19","key":"10.3233\/IDT-210227_ref8","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"issue":"4","key":"10.3233\/IDT-210227_ref9","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.compbiolchem.2010.07.002","article-title":"Stable feature selection for biomarker discovery","volume":"34","author":"He","year":"2010","journal-title":"Computational Biology and Chemistry."},{"key":"10.3233\/IDT-210227_ref10","doi-asserted-by":"crossref","unstructured":"Guyon I, Gunn S, Masoud Nikravesh L, Zadeh A. Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing. Springer-Verlag New York, Inc. Secaucus, NJ. 2006.","DOI":"10.1007\/978-3-540-35488-8"},{"key":"10.3233\/IDT-210227_ref11","doi-asserted-by":"crossref","unstructured":"Devi Arockia Vanitha C, Devaraj D, Venkatesulu M. Gene expression data classification using support vector machine and mutual information-based gene selection. Procedia Computer Science. 2015; 47: 13-21.","DOI":"10.1016\/j.procs.2015.03.178"},{"key":"10.3233\/IDT-210227_ref12","unstructured":"Fu T. A Comprehensive Comparison of Neural Network-Based Feature Selection Methods in Biological Omics Datasets. In 4th International Conference on Signal Processing and Machine Learning (SPML 2021). Beijing, China. ACM, New York, NY, USA. 2021."},{"key":"10.3233\/IDT-210227_ref13","doi-asserted-by":"crossref","unstructured":"Panagoulias DP, Sotiropoulos DN, Tsihrintzis GA. Biomarker-based deep learning for personalized nutrition. Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence (IEEE-ICTAI-2021). 2021.","DOI":"10.1109\/ICTAI52525.2021.00051"},{"key":"10.3233\/IDT-210227_ref14","unstructured":"Zhou Y, Porwal U, Zhang C, Ngo H, Nguyen X, R\u00e9 C, Govindaraju V. Parallel feature selection inspired by group testing. Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada. 2014, pp.\u00a03554-3562."},{"key":"10.3233\/IDT-210227_ref15","doi-asserted-by":"crossref","unstructured":"Tomczak K, Czerwi\u0144ska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. 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Academic Press, California. 2010."}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-210227","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:23:20Z","timestamp":1777454600000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-210227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,17]]},"references-count":18,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/idt-210227","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,17]]}}}