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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan / Hassani-Pak, Keywan


CiteScore 2018: 0.90

SCImago Journal Rank (SJR) 2018: 0.315

Open Access
Online
ISSN
1613-4516
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Volume 13, Issue 5

Issues

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

Müşerref Duygu Saçar Demirci / Mustafa Toprak / Jens Allmer
  • Corresponding author
  • Molecular Biology and Genetics, Izmir Institute of Technology, Urla, Izmir, Turkey
  • Bionia Incorporated, IZTEKGEB A8, Urla, Izmir, Turkey
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Published Online: 2017-04-20 | DOI: https://doi.org/10.1515/jib-2016-303

Summary

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.

About the article

Published Online: 2017-04-20

Published in Print: 2016-12-01


Citation Information: Journal of Integrative Bioinformatics, Volume 13, Issue 5, Pages 3–10, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2016-303.

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© 2016 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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[1]
Müşerref Duygu Saçar Demirci
Anadolu University Journal of Science and Technology-A Applied Sciences and Engineering, 2018, Page 1

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