Universiti Teknologi Malaysia Institutional Repository

One-class support vector machines for protein-protein interactions prediction

Alashwal, H. and Deris, Safaai and Othman, M. R. (2006) One-class support vector machines for protein-protein interactions prediction. International Journal of Biological and Medical Sciences, 1 (2). pp. 120-127. ISSN 1307-7457


Official URL: http://www.waset.org/ijbms/


Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vectormmachines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that proteinprotein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.

Item Type:Article
Uncontrolled Keywords:bioinformatics, protein-protein interactions, one- class support vector machines
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:8405
Deposited On:04 May 2009 07:55
Last Modified:16 Oct 2017 04:23

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