Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9453
Title: Distinguishing between MicroRNA targets from diverse species using sequence motifs and K-mers
Authors: Yousef, Malik
Khalifa, Waleed
Acar, İlhan Erkin
Allmer, Jens
Keywords: MicroRNA
Target Prediction
Motif
Machine Learning
Issue Date: 2017
Publisher: SciTePress
Abstract: A disease phenotype is often due to dysregulation of gene expression. Post-translational regulation of protein abundance by microRNAs (miRNAs) is, therefore, of high importance in, for example, cancer studies. MicroRNAs provide a complementary sequence to their target messenger RNA (mRNA) as part of a complex molecular machinery. Known miRNAs and targets are listed in miRTarBase for a variety of organisms. The experimental detection of such pairs is convoluted and, therefore, their computational detection is desired which is complicated by missing negative data. For machine learning, many features for parameterization of the miRNA targets are available and k-mers and sequence motifs have previously been used. Unrelated organisms like intracellular pathogens and their hosts may communicate via miRNAs and, therefore, we investigated whether miRNA targets from one species can be differentiated from miRNA targets of another. To achieve this end, we employed target information of one species as positive and the other as negative training and testing data. Models of species with higher evolutionary distance generally achieved better results of up to 97% average accuracy (mouse versus Caenorhabditis elegans) while more closely related species did not lead to successful models (human versus mouse; 60%). In the future, when more targeting data becomes available, models can be established which will be able to more precisely determine miRNA targets in hostpathogen systems using this approach.
Description: 10th International Joint Conference on Biomedical Engineering Systems and Technologies -- FEB 21-23, 2017 -- Porto, PORTUGAL
Acar, Ilhan Erkin/0000-0002-2078-9905
WOS: 000413258500013
URI: https://doi.org/10.5220/0006137901330139
https://hdl.handle.net/11147/9453
ISBN: 978-989-758-214-1
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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