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dc.contributor.authorSaçar, Müşerref Duygu
dc.contributor.authorHamzeiy, Hamid
dc.contributor.authorAllmer, Jens
dc.date.accessioned2017-04-07T07:40:38Z
dc.date.available2017-04-07T07:40:38Z
dc.date.issued2013
dc.identifier.citationSaçar, M. D., Hamzeiy, H., and Allmer, J. (2013). Can MiRBase provide positive data for machine learning for the detection of MiRNA hairpins? Journal of integrative bioinformatics, 10(2). doi:10.2390/biecoll-jib-2013-215en_US
dc.identifier.issn1613-4516
dc.identifier.urihttp://doi.org/10.2390/biecoll-jib-2013-215
dc.identifier.urihttp://hdl.handle.net/11147/5250
dc.description.abstractExperimental detection and validation of miRNAs is a tedious, time-consuming, and expensive process. Computational methods for miRNA gene detection are being developed so that the number of candidates that need experimental validation can be reduced to a manageable amount. Computational methods involve homology-based and ab inito algorithms. Both approaches are dependent on positive and negative training examples. Positive examples are usually derived from miRBase, the main resource for experimentally validated miRNAs. We encountered some problems with miRBase which we would like to report here. Some problems, among others, we encountered are that folds presented in miRBase are not always the fold with the minimum free energy; some entries do not seem to conform to expectations of miRNAs, and some external accession numbers are not valid. In addition, we compared the prediction accuracy for the same negative dataset when the positive data came from miRBase or miRTarBase and found that the latter led to more precise prediction models. We suggest that miRBase should introduce some automated facilities for ensuring data quality to overcome these problems.en_US
dc.language.isoengen_US
dc.publisherInformationsmanagement in der Biotechnologie e.V.en_US
dc.relation.isversionof10.2390/biecoll-jib-2013-215en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMicroRNAsen_US
dc.subjectSequence alignmenten_US
dc.subjectMolecular sequence dataen_US
dc.subjectNucleic aciden_US
dc.subjectDatabasesen_US
dc.titleCan MiRBase provide positive data for machine learning for the detection of MiRNA hairpins?en_US
dc.typearticleen_US
dc.contributor.authorIDTR114170en_US
dc.contributor.authorIDTR107974en_US
dc.contributor.iztechauthorSaçar, Müşerref Duygu
dc.contributor.iztechauthorHamzeiy, Hamid
dc.contributor.iztechauthorAllmer, Jens
dc.relation.journalJournal of integrative bioinformaticsen_US
dc.contributor.departmentIzmir Institute of Technology. Molecular Biology and Geneticsen_US
dc.identifier.volume10en_US
dc.identifier.issue2en_US
dc.identifier.scopusSCOPUS:2-s2.0-84891760538
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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