Title: Predicting protein-protein interactions by weighted pseudo amino acid composition
Authors: Yunus Emre Göktepe; İlhan İlhan; Şirzat Kahramanlı
Addresses: Seydisehir Vocational High School, Necmettin Erbakan University, Konya, Turkey ' Department of Mechatronics Engineering, Faculty of Engineering and Architecture, Necmettin Erbakan University, Konya, Turkey ' Department of Computer Education and Instructional Technologies, Mevlana University, Konya, Turkey
Abstract: Protein-protein interactions hold very important roles in biological processes. Prediction of PPIs is important for understanding these processes. In this context, substantive representations of proteins are needed during the process of interaction prediction in order to achieve higher prediction accuracy. In this paper, a new feature representation method, based on the concept of Chou's pseudo amino acid composition, was introduced. It is composed of the weighted amino acid composition information and the correlation factors of the protein. Finally, an SVM classification model was constructed for predicting PPIs. Experimental results exhibit that our method precedes those previously published in the literature.
Keywords: protein-protein interactions; PPIs; protein feature extraction; pseudo amino acid composition; support vector machines; SVM classification; feature representation; bioinformatics; PPI prediction.
DOI: 10.1504/IJDMB.2016.077073
International Journal of Data Mining and Bioinformatics, 2016 Vol.15 No.3, pp.272 - 290
Received: 31 Jan 2015
Accepted: 14 Mar 2016
Published online: 20 Jun 2016 *