A mixture of physicochemical and evolutionary–based feature extraction approaches for protein fold recognition. (18th December 2014)

• Record Type: Journal Article
• Authors: Dehzangi, Abdollah; Sharma, Alok; Lyons, James; Paliwal, Kuldip K.; Sattar, Abdul
• Is Part Of: International journal of data mining and bioinformatics. Volume 11:Number 1(2015)
• Page Start: 115
• Page End: 138
• Publication Date: 2014-12-18
• Subjects: protein fold recognition -- PFR -- feature selection -- feature extraction models -- segmented–based distribution -- segmented–based density -- evolutionary based features -- physicochemical based features -- bioinformatics -- protein structure prediction -- drug design; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Recent advancement in the pattern recognition field stimulates enormous interest in Protein Fold Recognition (PFR). PFR is considered as a crucial step towards protein structure prediction and drug design. Despite all the recent achievements, the PFR still remains as an unsolved issue in biological science and its prediction accuracy still remains unsatisfactory. Furthermore, the impact of using a wide range of physicochemical–based attributes on the PFR has not been adequately explored. In this study, we propose a novel mixture of physicochemical and evolutionary–based feature extraction methods based on the concepts of segmented distribution and density. We also explore the impact of 55 different physicochemical–based attributes on the PFR. Our results show that by providing more local discriminatory information as well as obtaining benefit from both physicochemical and evolutionary–based features simultaneously, we can enhance the protein fold prediction accuracy up to 5% better than previously reported results found in the literature.