Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information

• Published in: Journal of cheminformatics 2017-08, Vol.9 (1), p.47-47, Article 47
• Main Authors: An, Ji-Yong, Zhang, Lei, Zhou, Yong, Zhao, Yu-Jun, Wang, Da-Fu
• Format: Article
• Language: English
• Subjects: Amino acid sequence; Biological activity; Biological evolution; Chemistry; Chemistry and Materials Science; Classifiers; Computation; Computational Biology/Bioinformatics; Computational chemistry; Computer applications; Computer Applications in Chemistry; Datasets; Documentation and Information in Chemistry; Evolution; Feature extraction; Innovations; Internet; Learning algorithms; Local average group; Machine learning; Noise reduction; PCA; Predictions; Principal components analysis; Protein-protein interactions; Proteins; Research Article; Servers; SIPs; Support vector machines; Technology application; Theoretical and Computational Chemistry; Weighed-extreme learning machine; Yeast
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-017-0233-z

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM–LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM–LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .