A novel method for prediction of protein interaction sites based on integrated RBF neural networks.
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.
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jiwei road 106, Jinan 250022, Shandong, PR China. yhchen@ujn.edu.cn
Yuehui
Y
Chen
Jingru
J
Xu
Bin
B
Yang
Yaou
Y
Zhao
Wenxing
W
He
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D016571:Neural Networks, Computer; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D060388:Support Vector Machine
D011506:Proteins