@article{(Open Science Index):https://publications.waset.org/pdf/13827,
	  title     = {One-Class Support Vector Machines for Protein-Protein Interactions Prediction },
	  author    = {Hany Alashwal and  Safaai Deris and  Razib M. Othman},
	  country	= {},
	  institution	= {},
	  abstract     = {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 vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples. },
	    journal   = {International Journal of Bioengineering and Life Sciences},
	  volume    = {1},
	  number    = {3},
	  year      = {2007},
	  pages     = {192 - 199},
	  ee        = {https://publications.waset.org/pdf/13827},
	  url   	= {https://publications.waset.org/vol/3},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 3, 2007},
	}