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Title: Improving Positive Unlabeled Learning Algorithms for Protein Interaction Prediction
Authors: Pancaroglu, Doruk
Tan, Mehmet
Keywords: Protein Interaction Networks
Binary Classification
Positive Unlabeled Learning
Random Forests
Support Vector Machines
Issue Date: 2014
Publisher: SPRINGER-Verlag Berlin
Source: Pancaroglu, 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.
Abstract: In 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.
Description: 8th International Conference on Practical Applications of Computational Biology and Bioinformatics (2014 : Salamanca; Spain)
ISBN: 978-3-319-07581-5
ISSN: 2194-5357
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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