A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes

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doi doi:10.2390/biecoll-jib-2016-303
submission November 18, 2016
last revision December 08, 2016
published December 22, 2016
NCBI PubMed PubMed ID 28187417

Müserref Duygu Saçar Demirci, Mustafa Toprak and Jens Allmer

Correspondence should be addressed to:
Jens Allmer
Izmir Institute of Technology, Molecular Biology and Genetics, Urla, Izmir 35430, Turkey
ed.remlla@nullsnej


Abstract

Identification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.

Reference

Müserref Duygu Saçar Demirci, Mustafa Toprak and Jens Allmer. A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes. Journal of Integrative Bioinformatics, 13(5):303, 2016. Online Journal: http://journal.imbio.de/index.php?paper_id=303
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