Small endogenous non-coding RNAs called microRNAs (miRNAs) serve as the universal specificity elements in post-transcriptional gene silencing. Understanding the normal biological activities of miRNAs and their involvement in the emergence of illness have depended critically on the discovery of miRNAs, the identification of their targets, and subsequent inference of miRNA functions. In this study, we concentrate on computational approaches for integrating heterogeneous data sources to infer miRNA functions, including as miRNA functional annotation and inferring miRNA regulatory modules. We also give a brief overview of the work in the fields of miRNA discovery and miRNA-target identification, focusing on the difficulties in computational biology (Price JH et al., 2002).In order to empirically discover the genes that are downregulated by 25 miRNAs, we conduct a large-scale RNA sequencing investigation. To systematically identify miRNA targeting characteristics that are indicative of both miRNA binding and target downregulation, this RNA-seq dataset is coupled with publicly available miRNA target binding data. We create and verify a better computational model for the prediction of miRNA targets across the genome by incorporating these common traits into a machine learning framework (Kutay H et al., 2006).
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