By Topic

Novel weighted amino acid composition for prediction of protein structural classes within the context of multi-sensor data fusion approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Huseyin Seker ; Bio-Health Informatics Research Group, Centre for Computational Intelligence, School of Computing, De Montfort University, Leicester, LE1 9BH, UK

Prediction of structural classes of proteins is one of the most important but challenging research problems in computational biology and mainly based on amino acid sequence of the proteins. However, most of the predictive features based on the sequences donpsilat consider natural amino acid scales, which have been shown to play an important role in characterising the proteins in other studies. Therefore, this paper aims to present development of a novel weighted amino acid composition features based on the amino acid scales and multi-sensor data fusion strategies for reliable and accurate prediction of the structural classes of the proteins. The approach is further developed applying principal component analysis in each weighted amino acid composition features, which then leades to a locally optimized multi-sensor data fusion model. This pilot study presents promising results that show that the methods improve predictive accuracy by 1 to 10% compared to previously studied methods for the same data set. The approach taken is also shown to be not only effective, but also, in particular, more informative as it fuses information obtained from natural amino acid index scales that help better understand nature of such proteins.

Published in:

BioInformatics and BioEngineering, 2008. BIBE 2008. 8th IEEE International Conference on

Date of Conference:

8-10 Oct. 2008