Machine learning techniques in biological data classification and clustering: Initiation of a scientific voyage

Machine learning (ML) techniques have revolutionized the way of data classification, clustering, segregation, and novel element identification. ML techniques are having tremendous impetus for biological complex data classification. A number of studies reported novel data classification methods, complex biological element classification, and clustering. The present article briefs our experience in classifying biological species based on the biomarker genes and important proteins using state-of-the-art machine learning algorithms including artificial neural networks, support vector machines, decision trees, Bayesian methods, etc. Increased complexity warranted thorough human investigations and inspection to have a better classification on a case-by-case basis. Obtained outcomes were satisfactory and yielded novel strategies along with identifying the comparative superiority of specific algorithms for the specific datasets. However, obtaining a universal method or strategy remains the future objective. Automation of the process and precision increment for classification and clustering of the multi-parametric complex biological datasets are the other future goals.