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Data mining offers a highly effective technique that is useful in research and development of bioinformatics. Bioinformatics consists biological information such as DNA, RNA, and protein. Data mining tasks/techniques are classification, prediction, clustering, association, outlier detection, regression, and pattern tracking. Data mining provides important correlation, hidden patterns, and knowledge from the bioinformatics data set. This paper presents the role of data mining techniques in bioinformatics application. Classification of gene and protein structure, analyzing the gene expression, association of co-disease, outlier detection and gene selection, protein structure prediction, and drug discovery are some typical biological example that has proven data mining as a suitable technique for bioinformatics.
Bah, S. Y., Morang’a, C. M., Kengne-Ouafo, J. A., Amenga–Etego, L., & Awandare, G. A. (2018). Highlights on the application of genomics and bioinformatics in the fight against infectious diseases: Challenges and opportunities in Africa. Frontiers in Genetics, 9, 575. 30538723.
Bayat, A. (2002). Science, medicine, and the future: Bioinformatics. BMJ (Clinical Research Ed.), 324(7344), 1018–1022. 11976246.
David, S. K., Saeb, A. T., Rafiullah, M., & Rubeaan, K. (2019). Classification Techniques and Data Mining Tools Used in Medical Bioinformatics. In Big Data Governance and Perspectives in Knowledge Management (pp. 105–126). IGI Global.
de Ridder, D., de Ridder, J., & Reinders, M. J. (2013). Pattern recognition in bioinformatics. Briefings in Bioinformatics, 14(5), 633–647. 23559637.