MiRANN: a reliable approach for improved classification of precursor microRNA using Artificial Neural Network model

Genomics
Md Eamin RahmanMd Ruhul Amin

Abstract

MicroRNA (miRNA) is a special class of short noncoding RNA that serves pivotal function of regulating gene expression. The computational prediction of new miRNA candidates involves various methods such as learning methods and methods using expression data. This article has proposed a reliable model - miRANN which is a supervised machine learning approach. MiRANN used known pre-miRNAs as positive set and a novel negative set from human CDS regions. The number of known miRNAs is now huge and diversified that could cover almost all characteristics of unknown miRNAs which increases the quality of the result (99.9% accuracy, 99.8% sensitivity, 100% specificity) and provides a more reliable prediction. MiRANN performs better than other state-of-the-art approaches and declares to be the most potential tool to predict novel miRNAs. We have also tested our result using a previous negative set. MiRANN, opens new ground using ANN for predicting pre-miRNAs with a promise of better performance.

References

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Citations

Dec 29, 2015·Journal of Bioinformatics and Computational Biology·Cong PianLiang-Yun Zhang
Dec 6, 2016·Genes·Qaisar AbbasMuhammad Arfan Jaffar
Jul 19, 2019·Reviews in the Neurosciences·Jian-Jiao ChenShao Li
Dec 31, 2018·Theoretical Biology & Medical Modelling·Edgar Manzanarez-OzunaPatricia Juárez
Jan 15, 2020·BMC Bioinformatics·Xueming ZhengMeng Wang
May 26, 2018·Briefings in Bioinformatics·Georgina StegmayerDiego H Milone

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