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Title: Biological Network Derivation by Positive Unlabeled Learning Algorithms
Authors: Pancaroglu, Doruk
Tan, Mehmet
Keywords: Binary Classification
Positive Unlabeled Learning
Protein-Ligand İnteraction Networks
Protein-Protein İnteraction Networks
Random Forests
Support Vector Machines
Issue Date: 2016
Publisher: Bentham Bcience Publ. Ltd.
Source: Pancaroglu, D., & Tan, M. (2016). Biological Network Derivation by Positive Unlabeled Learning Algorithms. Current Bioinformatics, 11(5), 531-536.
Abstract: Background: 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.
ISSN: 1574-8936
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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