Protein fold recognition using neural networks and support vector machines

Authors:
Nan Jiang;Wendy Xinyu Wu;Ian Mitchell
Affiliations:
School of Computing Science, Middlesex University, London, UK;School of Computing Science, Middlesex University, London, UK;School of Computing Science, Middlesex University, London, UK
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 3
Cited 0

Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
Protein threading with residue-environment matching by artificial neural networks

Proceedings of the 2004 ACM symposium on Applied computing
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)

Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new fold recognition model with mixed environment-specific substitution mapping (called MESSM) is proposed with three key features: 1) a structurally-derived substitution score is generated using neural networks; 2) a mixed environment-specific substitution mapping is developed by combing the structural-derived substitution score with sequence profile from well-developed sequence substitution matrices; 3) a support vector machine is employed to measure the significance of the sequence-structure alignment. Tested on two benchmark problems, the MESSM model shows comparable performance to those more computational intensive, energy potential based fold recognition models. The results also demonstrate that the new fold recognition model with mixed substitution mapping has a better performance than the one with either structure or sequence profile only. The MESSM model presents a new way to develop an efficient tool for protein fold recognition.