Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 42, Issue 4, Pages 402-407Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2011.12.007
Keywords
Protein interaction sites; RBF neural networks; Integrate; Sliding window; Particle swarm optimization
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Funding
- Natural Science Foundation of China [61070130]
- Natural Science Foundation of Shandong Province [ZR2011FZ003]
- Key Subject Research Foundation of Shandong Province
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Protein interactions are very important for control life activities. If we want to study the principle of protein interactions, we have to find the seats of a protein which are involved in the interactions called interaction sites firstly. In this paper, a novel method based on an integrated RBF neural networks is proposed for prediction of protein interaction sites. At first, a number of features were extracted, i.e., sequence profiles, entropy, relative entropy, conservation weight, accessible surface area and sequence variability. Then 6 sliding windows about these features were made, and they contained 1, 3, 5, 7, 9 and 11 amino acid residues respectively. These sliding windows were put into the input layers of six radial basis functional neural networks that were optimized by Particle Swarm Optimization. Thus, six group results were obtained. Finally, these six group results were integrated by decision fusion (DF) and Genetic Algorithm based Selective Ensemble (GASEN). The experimental results show that the proposed method performs better than the other related methods such as neural networks and support vector machine. (c) 2011 Elsevier Ltd. All rights reserved.
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