Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5322
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSaçar, Müşerref Duygu-
dc.contributor.authorAllmer, Jens-
dc.date.accessioned2017-04-17T11:24:11Z
dc.date.available2017-04-17T11:24:11Z
dc.date.issued2013
dc.identifier.citationSaçar, M. D., and Allmer, J. (2013, September 25-27). Data mining for microrna gene prediction: On the impact of class imbalance and feature number for microrna gene prediction. Paper presented at the 8th International Symposium on Health Informatics and Bioinformatics. doi:10.1109/HIBIT.2013.6661685en_US
dc.identifier.isbn9781479907014
dc.identifier.urihttp://doi.org/10.1109/HIBIT.2013.6661685
dc.identifier.urihttp://hdl.handle.net/11147/5322
dc.description8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013; Ankara; Turkey; 25 September 2013 through 27 September 2013en_US
dc.description.abstractMicroRNAs (miRNAs) are small, non-coding RNAs which are involved in the posttranscriptional modulation of gene expression. Their short (18-24) single stranded mature sequences are involved in targeting specific genes. It turns out that experimental methods are limited and that it is difficult, if not impossible, to establish all miRNAs and their targets experimentally. Therefore, many tools for the prediction of miRNA genes and miRNA targets have been proposed. Most of these tools are based on machine learning methods and within that area mostly two-class classification is employed. Unfortunately, truly negative data is impossible to attain and only approximations of negative data are currently available. Also, we recently showed that the available positive data is not flawless. Here we investigate the impact of class imbalance on the learner accuracy and find that there is a difference of up to 50% between the best and worst precision and recall values. In addition, we looked at increasing number of features and found a curve maximizing at 0.97 recall and 0.91 precision with quickly decaying performance after inclusion of more than 100 features. © 2013 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof8th International Symposium on Health Informatics and Bioinformatics, HIBIT 2013en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClass imbalanceen_US
dc.subjectData miningen_US
dc.subjectFeature selectionen_US
dc.subjectMachine learningen_US
dc.subjectMicroRNAsen_US
dc.subjectMiRNA gene predictionen_US
dc.titleData mining for microrna gene prediction: On the impact of class imbalance and feature number for microrna gene predictionen_US
dc.typeConference Objecten_US
dc.authoridTR114170en_US
dc.authoridTR107974en_US
dc.institutionauthorSaçar, Müşerref Duygu-
dc.institutionauthorAllmer, Jens-
dc.departmentİzmir Institute of Technology. Molecular Biology and Geneticsen_US
dc.identifier.scopus2-s2.0-84892650223en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/HIBIT.2013.6661685-
dc.relation.doi10.1109/HIBIT.2013.6661685en_US
dc.coverage.doi10.1109/HIBIT.2013.6661685en_US
dc.identifier.wosqualityN/A-
dc.identifier.scopusqualityN/A-
item.grantfulltextopen-
item.openairetypeConference Object-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept04.03. Department of Molecular Biology and Genetics-
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
5322.pdfConference Paper880.1 kBAdobe PDFThumbnail
View/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

19
checked on Jul 19, 2024

Page view(s)

166
checked on Jul 15, 2024

Download(s)

220
checked on Jul 15, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.