Abstract

RNA interference (RNAi) and gene inactivation are extensively used biological terms in biomedical research. Two categories of small ribonucleic acid (RNA) molecules, viz., microRNA (miRNA) and small interfering RNA (siRNA) are central to the RNAi. There are various kinds of algorithms developed related to RNAi and gene silencing. In this book chapter, we provided a comprehensive review of various machine learning and association rule mining algorithms developed to handle different biological problems such as detection of gene signature, biomarker, gene module, potentially disordered protein, differentially methylated region and many more. We also provided a comparative study of different well-known classifiers along with other used methods. In addition, we demonstrated the brief biological information regarding the immense biological challenges for gene activation as well as their advantages, disadvantages and possible therapeutic strategies. Finally, our study helps the bioinformaticians to understand the overall immense idea in different research dimensions including several learning algorithms for the benevolent of the disease discovery.

Keywords: machine learning, association rule mining, RNAi, gene silencing, multi-omics data
