Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9453
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dc.contributor.authorYousef, Malik-
dc.contributor.authorKhalifa, Waleed-
dc.contributor.authorAcar, İlhan Erkin-
dc.contributor.authorAllmer, Jens-
dc.date.accessioned2020-07-25T22:12:32Z-
dc.date.available2020-07-25T22:12:32Z-
dc.date.issued2017-
dc.identifier.isbn978-989-758-214-1-
dc.identifier.urihttps://doi.org/10.5220/0006137901330139-
dc.identifier.urihttps://hdl.handle.net/11147/9453-
dc.description8th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2017 - Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017en_US
dc.description.abstractA 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.en_US
dc.language.isoenen_US
dc.publisherSCITEPRESSen_US
dc.relation.ispartof10th International Joint Conference on Biomedical Engineering Systems and Technologiesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMicroRNAen_US
dc.subjectTarget Predictionen_US
dc.subjectMotifen_US
dc.subjectMachine Learningen_US
dc.titleDistinguishing between MicroRNA targets from diverse species using sequence motifs and K-mersen_US
dc.typeConference Objecten_US
dc.institutionauthorAcar, İlhan Erkin-
dc.institutionauthorAllmer, Jens-
dc.departmentİzmir Institute of Technology. Molecular Biology and Geneticsen_US
dc.departmentİzmir Institute of Technology. Bioengineeringen_US
dc.identifier.startpage133en_US
dc.identifier.endpage139en_US
dc.identifier.wosWOS:000413258500013en_US
dc.identifier.scopus2-s2.0-85015703053en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.5220/0006137901330139-
dc.relation.doi10.5220/0006137901330139en_US
dc.coverage.doi10.5220/0006137901330139en_US
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept01. Izmir Institute of Technology-
crisitem.author.dept04.03. Department of Molecular Biology and Genetics-
Appears in Collections:Bioengineering / Biyomühendislik
Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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