MESSM: a framework for protein fold recognition using Neural Networks and Support Vector Machines

Authors:
Nan Jiang;Wendy Wu;Ian Mitchell
Affiliations:
School of Computing Science, Middlesex University, London EN4 4BT, UK.;School of Computing Science, Middlesex University, London EN4 4BT, UK.;School of Computing Science, Middlesex University, London EN4 4BT, UK
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2006

Citing 6
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Making large-scale support vector machine learning practical

Advances in kernel methods
Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)

Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Threading with environment-specific score by artificial neural networks

Soft Computing - A Fusion of Foundations, Methodologies and Applications

Protein fold recognition with a two-layer method based on SVM-SA, WP-NN and C4.5 TLM-SNC

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new framework (called MESSM) for protein fold recognition with three key features is proposed in this paper. Being tested on three benchmark problems, the results show that the MESSM has a comparable performance on fold recognition to those more computational intensive, energy potential based fold recognition models. The MESSM leads to a better performance on alignment accuracy. The MESSM presents a new way to develop an efficient tool for protein fold recognition.