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dc.contributor.authorYousef, Malik
dc.contributor.authorAllmer, Jens
dc.contributor.authorKhalifa, Waleed
dc.date.accessioned2017-06-28T07:35:59Z
dc.date.available2017-06-28T07:35:59Z
dc.date.issued2016
dc.identifier.citationYousef, M., Allmer, J., and Khalifa, W. (2016, February 21-23). Feature selection for microRNA target prediction comparison of one-class feature selection methodologies. Paper presented at the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016. doi:10.5220/0005701602160225en_US
dc.identifier.isbn9789897581700
dc.identifier.urihttp://doi.org/10.5220/0005701602160225
dc.identifier.urihttp://hdl.handle.net/11147/5792
dc.description7th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2016 - Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016; Rome; Italy; 21 February 2016 through 23 February 2016en_US
dc.description.abstractTraditionally, machine learning algorithms build classification models from positive and negative examples. Recently, one-class classification (OCC) receives increasing attention in machine learning for problems where the negative class cannot be defined unambiguously. This is specifically problematic in bioinformatics since for some important biological problems the target class (positive class) is easy to obtain while the negative one cannot be measured. Artificially generating the negative class data can be based on unreliable assumptions. Several studies have applied two-class machine learning to predict microRNAs (miRNAs) and their target. Different approaches for the generation of an artificial negative class have been applied, but may lead to a biased performance estimate. Feature selection has been well studied for the two-class classification problem, while fewer methods are available for feature selection in respect to OCC. In this study, we present a feature selection approach for applying one-class classification to the prediction of miRNA targets. A comparison between one-class and two-class approaches is presented to highlight that their performance are similar while one-class classification is not based on questionable artificial data for training and performance evaluation. We further show that the feature selection method we tried works to a degree, but needs improvement in the future. Perhaps it could be combined with other approaches.en_US
dc.description.sponsorshipThe Scientific and Technological Research Council of Turkey [grant number 113E326]en_US
dc.language.isoengen_US
dc.publisherSciTePressen_US
dc.relationinfo:eu-repo/grantAgreement/TUBITAK/EEEAG/113E326en_US
dc.relation.isversionof10.5220/0005701602160225en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature selectionen_US
dc.subjectMachine learningen_US
dc.subjectMicroRNA targetsen_US
dc.subjectClassificationen_US
dc.subjectBioinformaticsen_US
dc.titleFeature selection for microRNA target prediction comparison of one-class feature selection methodologiesen_US
dc.typeconferenceObjecten_US
dc.contributor.authorIDTR107974en_US
dc.contributor.iztechauthorAllmer, Jens
dc.relation.journal9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016en_US
dc.contributor.departmentİYTE, Fen Fakültesi, Moleküler Biyoloji ve Genetik Bölümüen_US
dc.identifier.scopusSCOPUS:2-s2.0-84969228214
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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