Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9340
Title: A machine learning approach for MicroRNA precursor prediction in retro-transcribing virus genomes
Authors: Saçar Demirci, Müşerref Duygu
Toprak, Mustafa
Allmer, Jens
Issue Date: 2016
Publisher: Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.)
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.
URI: https://doi.org/10.2390/biecoll-jib-2016-303
https://hdl.handle.net/11147/9340
ISSN: 1613-4516
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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