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Authors: Malik Yousef 1 ; Waleed Khalifa 2 ; İlhan Erkin Acar 3 and Jens Allmer 4

Affiliations: 1 Zefat Academic College, Israel ; 2 The College of Sakhnin, Israel ; 3 Izmir Institute of Technology, Turkey ; 4 Izmir Institute of Technology and Bionia Incorporated, Turkey

ISBN: 978-989-758-214-1

ISSN: 2184-4305

Keyword(s): MicroRNA, Target Prediction, Motif, Machine Learning.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Pattern Recognition, Clustering and Classification ; Sequence Analysis

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 speci es 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 \textit{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. (More)

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Paper citation in several formats:
Yousef, M.; Khalifa, W.; Acar, İ. and Allmer, J. (2017). Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1 ISSN 2184-4305, pages 133-139. DOI: 10.5220/0006137901330139

@conference{bioinformatics17,
author={Malik Yousef. and Waleed Khalifa. and İlhan Erkin Acar. and Jens Allmer.},
title={Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={133-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006137901330139},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers
SN - 978-989-758-214-1
IS - 2184-4305
AU - Yousef, M.
AU - Khalifa, W.
AU - Acar, İ.
AU - Allmer, J.
PY - 2017
SP - 133
EP - 139
DO - 10.5220/0006137901330139

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