Brief Communication: A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction

Authors:
Sitanshu Sekhar Sahu;Ganapati Panda
Affiliations:
Department of Electronics and Communication Engineering, National Institute of Technology, Orissa, India;School of Electrical Sciences, Indian Institute of Technology, Orissa, India
Venue:
Computational Biology and Chemistry
Year:
2010

Citing 4
Cited 5

Universal approximation using radial-basis-function networks

Neural Computation
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes

Bioinformatics
Algorithm Note: Variable predictive model based classification algorithm for effective separation of protein structural classes

Computational Biology and Chemistry
Feature extraction from tumor gene expression profiles using DCT and DFT

EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence

Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method

Artificial Intelligence in Medicine
CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition

Computational Biology and Chemistry
Research article: Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins

Computational Biology and Chemistry
Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

Computer Methods and Programs in Biomedicine
Ensemble classification of colon biopsy images based on information rich hybrid features

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

During last few decades accurate determination of protein structural class using a fast and suitable computational method has been a challenging problem in protein science. In this context a meaningful representation of a protein sample plays a key role in achieving higher prediction accuracy. In this paper based on the concept of Chou's pseudo amino acid composition (Chou, K.C., 2001. Proteins 43, 246-255), a new feature representation method is introduced which is composed of the amino acid composition information, the amphiphilic correlation factors and the spectral characteristics of the protein. Thus the sample of a protein is represented by a set of discrete components which incorporate both the sequence order and the length effect. On the basis of such a statistical framework a simple radial basis function network based classifier is introduced to predict protein structural class. A set of exhaustive simulation studies demonstrates high success rate of classification using the self-consistency and jackknife test on the benchmark datasets.