Journal Article

FEGS: a novel feature extraction model for protein sequences and its applications

Wei, Leyi, Liu, Juntao, Mu, Zengchao, Zheng, Hongyu, Yu, Ting, Liu, Xiaoping

2021

Overview

Background Feature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions. Results In this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods. Conclusion The FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Amino acid sequence / Amino acids / Bioinformatics / Biomedical and Life Sciences / Buffalo / Computational biology / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Datasets / Feature extraction / Graphic methods / Graphical representation / Graphical representations / Life Sciences / Mammals / Methods / Microarrays / Phylogenetics / Phylogeny / Physicochemical properties / Physicochemical properties of amino acids / Protein similarity analysis / Proteins / Statistical features / Statistics / Taxonomy