Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5892
Title: Positive unlabeled learning for deriving protein interaction networks
Authors: Kılıç, C.
Tan, M.
Issue Date: 2012
Publisher: Springer Verlag
Abstract: Binary 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.
URI: https://doi.org/10.1007/s13721-012-0012-8
https://hdl.handle.net/20.500.11851/5892
ISSN: 2192-6670
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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