Research Repository

Protein Secondary Structure Prediction Using Support Vector Machines and a New Feature Representation

Gubbi, Jayavardhana and Lai, Daniel and Palaniswami, Marimuthu and Parker, Michael (2006) Protein Secondary Structure Prediction Using Support Vector Machines and a New Feature Representation. International Journal of Computational Intelligence and Applications, 6 (4). pp. 551-567. ISSN 1469-0268

Full text for this resource is not available from the Research Repository.

Abstract

Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q3 accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on http://www.ee.unimelb.edu.au/ISSNIP/bioinf/.

Item Type: Article
Uncontrolled Keywords: ResPubID19009, protein secondary structure prediction; support vector machines; position specific scoring matrix (PSSM); Chou–Fasman parameters; Kyte–Doolittle hydrophobicity; Grantham polarity; reliability index; novel encoding scheme
Subjects: FOR Classification > 0903 Biomedical Engineering
Faculty/School/Research Centre/Department > School of Sport and Exercise Science
Related URLs:
Depositing User: VUIR
Date Deposited: 01 May 2012 02:54
Last Modified: 16 Mar 2015 04:41
URI: http://vuir.vu.edu.au/id/eprint/2984
ePrint Statistics: View download statistics for this item

Repository staff only

View Item View Item

Search Google Scholar