Deris, Safaai and Othman, Razib M. and Alashwal, Hany (2009) A bayesian kernel for the prediction of protein-protein interactions. International Journal of Computational Intelligence, 51 .
Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction canbe predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels.
|Uncontrolled Keywords:||bioinformatics, protein-protein interactions, bayesian kernel, support vector machines.|
|Subjects:||Q Science > Q Science (General)|
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
|Divisions:||Computer Science and Information System|
|Deposited By:||Ramli Haron|
|Deposited On:||15 Dec 2011 05:22|
|Last Modified:||15 Dec 2011 05:23|
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