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dc.contributor.authorPancaroglu, Doruk
dc.contributor.authorTan, Mehmet
dc.date.accessioned2019-07-10T14:42:46Z
dc.date.available2019-07-10T14:42:46Z
dc.date.issued2014
dc.identifier.citationPancaroglu, D., & Tan, M. (2014). Improving Positive Unlabeled Learning Algorithms for Protein Interaction Prediction. In 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014) (pp. 81-88). Springer, Cham.en_US
dc.identifier.isbn978-3-319-07581-5
dc.identifier.isbn978-3-319-07580-8
dc.identifier.issn2194-5357
dc.identifier.urihttps://link.springer.com/chapter/10.1007%2F978-3-319-07581-5_10
dc.identifier.urihttp://hdl.handle.net/20.500.11851/2024
dc.description8th International Conference on Practical Applications of Computational Biology and Bioinformatics (2014 : Salamanca; Spain)
dc.description.abstractIn binary classification, it is sometimes difficult to label two training samples as negative. The aforementioned difficulty in obtaining true negative samples created a need for learning algorithms which does not use negative samples. This study aims to improve upon two PU learning algorithms, AGPS[2] and Roc-SVM[3] for protein interaction prediction. Two extensions to these algorithms is proposed; the first one is to use Random Forests as the classifier instead of support vector machines and the second is to combine the results of AGPS and Roc-SVM using a voting system. After these two approaches are implemented, their results was compared to the original algorithms as well as two well-known learning algorithms, ARACNE [9] and CLR [10]. In the comparisons, both the Random Forest ( called AGPS-RF and Roc-RF) and the Hybrid algorithm performed well against the original SVM-classified ones. The improved algorithms also performed well against ARACNE and CLR.en_US
dc.language.isoengen_US
dc.publisherSPRINGER-Verlag Berlinen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectProtein Interaction Networksen_US
dc.subjectBinary Classificationen_US
dc.subjectPositive Unlabeled Learningen_US
dc.subjectRandom Forestsen_US
dc.subjectSupport Vector Machinesen_US
dc.titleImproving Positive Unlabeled Learning Algorithms for Protein Interaction Predictionen_US
dc.typeconferenceObjecten_US
dc.relation.journalAdvances in Intelligent Systems and Computingen_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.volume294
dc.identifier.startpage81
dc.identifier.endpage88
dc.contributor.orcidhttps://orcid.org/0000-0002-1741-0570
dc.identifier.wosWOS:000351276700010
dc.identifier.scopus2-s2.0-84921796211
dc.contributor.tobbetuauthorTan, Mehmet
dc.contributor.YOKid110845
dc.identifier.doi10.1007/978-3-319-07581-5_10
dc.contributor.wosresearcherIDI-2328-2019
dc.contributor.ScopusAuthorID36984623900
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıtr_TR


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