Show simple item record

dc.contributor.authorPancaroglu, Doruk
dc.contributor.authorTan, Mehmet
dc.date.accessioned2019-06-26T07:40:35Z
dc.date.available2019-06-26T07:40:35Z
dc.date.issued2016
dc.identifier.citationPancaroglu, D., & Tan, M. (2016). Biological Network Derivation by Positive Unlabeled Learning Algorithms. Current Bioinformatics, 11(5), 531-536.en_US
dc.identifier.issn1574-8936
dc.identifier.urihttp://www.eurekaselect.com/143364/article
dc.identifier.urihttp://hdl.handle.net/20.500.11851/1171
dc.description.abstractBackground: In cases where only a single group (or class) of samples is available for a given problem, positive unlabeled learning algorithms can be applied. One such case is the interactions between various biological/chemical entity pairs, where only the set of interacting entities can be collected, not the "non-interacting" ones. Objective: We aim to improve the performance of deriving protein-protein and protein-ligand interactions. We argue that the positive-unlabeled learning algorithms can be applied to this problem. Method: In this paper, we propose some modifications to two of the existing methods for protein-protein and protein-ligand interaction network derivation. First, we extend the algorithms to use Random Forests and then we devise an ensemble classifier from these two based on voting. Results: We report the evaluation results of the proposed algorithms in comparison to the original methods and well-known biological network derivation algorithms. We achieved significant improvements in terms of different metrics. Conclusion: The results are promising in the sense that proposed methods either perform competitively or better than previous methods. This motivates us in applying the proposed methods to other data sets and similar problems.en_US
dc.language.isoengen_US
dc.publisherBentham Bcience Publ. Ltd.en_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBinary Classificationen_US
dc.subjectPositive Unlabeled Learningen_US
dc.subjectProtein-Ligand İnteraction Networksen_US
dc.subjectProtein-Protein İnteraction Networksen_US
dc.subjectRandom Forestsen_US
dc.subjectSupport Vector Machinesen_US
dc.titleBiological Network Derivation by Positive Unlabeled Learning Algorithmsen_US
dc.typearticleen_US
dc.relation.journalCurrent Bioinformaticsen_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume11
dc.identifier.issue5
dc.identifier.startpage531
dc.identifier.endpage536
dc.contributor.orcidhttps://orcid.org/0000-0002-1741-0570
dc.identifier.wosWOS:000390342000005
dc.identifier.scopus2-s2.0-84995969135
dc.contributor.tobbetuauthorTan, Mehmet
dc.contributor.YOKid110845
dc.identifier.doi10.2174/1574893611666160617093509
dc.contributor.wosresearcherIDI-2328-2019
dc.contributor.ScopusAuthorID36984623900
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record