Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6634
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dc.contributor.authorErdoğdu, Utku-
dc.contributor.authorTan, Mehmet-
dc.contributor.authorAlhajj, Reda-
dc.contributor.authorPolat, Faruk-
dc.contributor.authorDemetrick, Douglas-
dc.contributor.authorRokne, Jon-
dc.date.accessioned2021-09-11T15:43:01Z-
dc.date.available2021-09-11T15:43:01Z-
dc.date.issued2011en_US
dc.identifier.citationIEEE International Conference on Bioinformatics and Biomedicine (BIBM) -- NOV 12-15, 2011 -- Atlanta, GAen_US
dc.identifier.isbn978-0-7695-4574-5-
dc.identifier.issn2156-1125-
dc.identifier.issn2156-1133-
dc.identifier.urihttps://doi.org/10.1109/BIBM.2011.105-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6634-
dc.description.abstractFor certain domains, e. g. bioinformatics, producing more real samples is costly, error prone and time consuming. Therefore, there is a need for an intelligent automated process capable of substituting the real samples by artificial samples that carry the same characteristics as the real samples and hence could be used for running comprehensive testing of new methodologies. Motivated by this need, we describe a novel approach that integrates Probabilistic Boolean Network and genetic algorithm based techniques into a framework that uses some existing real samples as input and successfully produces new samples as output. The new samples will inspire the characteristics of the existing samples without duplicating them. This leads to diversity in the samples and hence a more rich set of samples to be used in testing. The developed framework incorporates two models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples; a high demanding area that has not received attention. The two perspectives employed in the process are based on models that are not closely related; the independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.en_US
dc.description.sponsorshipIEEE, IEEE Comp Socen_US
dc.description.sponsorshipScientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [110E179]en_US
dc.description.sponsorshipThis work is partially supported by the Scientific and Technological Research Council of Turkey under Grant No. 110E179en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Socen_US
dc.relation.ispartof2011 IEEE International Conference On Bioinformatics And Biomedicine (Bibm 2011)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectgene expression dataen_US
dc.subjectsample generationen_US
dc.subjectlearningen_US
dc.subjectgenetic algorithmen_US
dc.subjectprobabilistic boolean networken_US
dc.titleEmploying Machine Learning Techniques for Data Enrichment: Increasing the number of samples for effective gene expression data analysisen_US
dc.typeConference Objecten_US
dc.relation.ispartofseriesIEEE International Conference on Bioinformatics and Biomedicine-BIBMen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage238en_US
dc.identifier.endpage242en_US
dc.authorid0000-0002-1741-0570-
dc.authorid0000-0003-0509-9153-
dc.identifier.wosWOS:000411330600043en_US
dc.identifier.scopus2-s2.0-84856055390en_US
dc.institutionauthorTan, Mehmet-
dc.identifier.doi10.1109/BIBM.2011.105-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conferenceIEEE International Conference on Bioinformatics and Biomedicine (BIBM)en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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