5. Conclusion

RNAi and gene inactivation are well-known research topics in the research of biomedical field. MiRNA and siRNA are closely associated with RNAi. Various categories of algorithms associated with RNAi and gene silencing have been developed in last 2 decades. In this book chapter, we provided a comprehensive review of various machine/deep learning as well as association rule mining algorithms that have been developed for handling different biological problems such as gene signature detection, multi-omics data integration, single/combinatorial biomarker identification, gene module detection, potentially disordered protein detection, differentially methylated region finding, and many more. Thereafter, a comparative study of several well-known classifiers along with other used approaches for the study has been included. In addition, we provided a brief biological description of the immense biological challenges for the gene activation along with their advantages, disadvantages and possible therapeutic strategies. Finally, this chapter helps the bioinformaticians to understand the central idea of RNAi and gene silencing along with their peripheral machine/deep learning and association rule mining algorithms for the benevolent of the disease discovery as well as possible therapeutic values.
