A combinatorial in silico approach for microRNA-target identification: Order out of chaos

Contemporary computational microRNA(miRNA)-target prediction tools have been playing a vital role in pursuing putative targets for a solitary miRNA or a group of miRNAs. These tools utilise a set of prob-abilistic algorithms, machine learning techniques and analyse experimentally validated miRNA targets to identify the potential miRNA-target pairs. Unfortunately, current tools generate a huge number of false-positive predictions. A standardized approach with a single tool or a combination of tools is still lacking. Moreover, sensitivity, specificity and overall efficiency of any single tool are yet to be satisfactory. Therefore, a systematic combination of selective online tools combining the factors regarding miRNA-target identification would be valuable as an miRNA-target prediction scheme. The focus of this study was to develop a theoretical framework by combining six available online tools to facilitate the current understanding of miRNA-target identification. (c) 2021 Elsevier B.V. and Soci & eacute;t & eacute; Fran & ccedil;aise de Biochimie et Biologie Mol & eacute;culaire (SFBBM). All rights reserved.

Automated summary

Contemporary computational miRNA-target prediction tools are essential in identifying potential targets for miRNAs, but they often generate numerous false-positive predictions. The lack of a standardized approach and satisfactory sensitivity, specificity, and overall efficiency in single tools calls for a systematic combination of selective online tools to develop an effective prediction scheme for miRNA-target identification.