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

Saçar Demirci, Müserref Duygu ; Toprak, Mustafa ; Allmer, Jens

Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516)


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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.


Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Saçar Demirci, Müserref Duygu ; Toprak, Mustafa ; Allmer, Jens  (2016)  A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes. Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516), 13(5): Special Issue: Computational miRNomics, 2016

Online-Journal: http://journal.imbio.de/article.php?aid=303
URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2017/5449



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