By Topic

Prediction of Protein B-Factor Profile Based on Feature Selection and Kernel Learning

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
$31 $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

2 Author(s)
Xiao-Yong Pan ; Inst. of Image Process. & Pattern Recognition, Shanghai Jiaotong Univ., Shanghai, China ; Hong-Bin Shen

B-factor reflects the atom's uncertainty about its average position within a crystal structure and is highly correlated with protein functions. In this article, we propose a novel approach to predict the real value of B-factor. We firstly extract features from the protein sequences and their evolution information, then apply random forest tree to select the important features, which are further inputted to a two-stage support vector regression (SVR) for prediction. Our results have revealed that a systematic analysis of the importance of different features makes us have deep insights into the different contributions of features and is very necessary for developing effective B-factor prediction tools. We thus develop an online Web server, which is freely available at http://www.csbio.situ.edu.cn/bioinf/PredBF for academic use.

Published in:

Pattern Recognition, 2009. CCPR 2009. Chinese Conference on

Date of Conference:

4-6 Nov. 2009