PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.

ABSTRACT: The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based machine-learning technique was applied for residue-level prediction of the domain/linker annotations in protein sequences using ordered/disordered regions along protein chains and a set of physicochemical properties. Six different classifiers-decision tree, Gaussian naïve Bayes, linear discriminant analysis, support vector machine, random forest, and multilayer perceptron-were exhaustively explored for the residue-level prediction of domain/linker regions. The protein sequences from the curated CATH database were used for training and cross-val

SUBMITTER: Chatterjee P 

PROVIDER: S-EPMC4788683 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature