Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5892
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dc.contributor.authorKılıç, C.-
dc.contributor.authorTan, M.-
dc.date.accessioned2021-09-11T15:20:37Z-
dc.date.available2021-09-11T15:20:37Z-
dc.date.issued2012en_US
dc.identifier.issn2192-6670-
dc.identifier.urihttps://doi.org/10.1007/s13721-012-0012-8-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/5892-
dc.description.abstractBinary classification is the process of labeling the members of a given data set on the basis of whether they have some property or not. To train a binary classifier, normally one needs two sets of examples from each group, usually named as positive and negative examples. However, in some domains, negative examples are either hard to obtain or even not available at all. In these problems, data consist of positive and unlabeled examples. This paper first presents a survey of algorithms which can handle such problems, and then it provides a comparison of some of these algorithms on the protein-protein interaction derivation problem by using the available (positive) interaction information. © 2012 Springer-Verlag.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofNetwork Modeling and Analysis in Health Informatics and Bioinformaticsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titlePositive Unlabeled Learning for Deriving Protein Interaction Networksen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume1en_US
dc.identifier.issue3en_US
dc.identifier.startpage87en_US
dc.identifier.endpage102en_US
dc.identifier.wosWOS:000447371800002en_US
dc.identifier.scopus2-s2.0-84995885612en_US
dc.institutionauthorTan, Mehmet-
dc.identifier.doi10.1007/s13721-012-0012-8-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept04.02. Department of International Entrepreneurship-
crisitem.author.dept02.1. Department of Artificial Intelligence 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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