Cart (Loading....) | Create Account
Close category search window
 

Predicting DNA-Binding Residues of Proteins Using Random Forest and Evolutionary Information Combined with Conservation Information

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

6 Author(s)
Xin Ma ; State Key Lab. of Bioelectronics, Southeast Univ., Nanjing, China ; Jing Guo ; Jian-Sheng Wu ; Hong-De Liu
more authors

Protein-DNA interactions play pivotal role in a variety of biological processes in cells. In this research, a novel prediction model is proposed for predicting DNA-binding residues from amino acids sequences using a variety of features from amino acid sequence information with random forest (RF) algorithm. A novel feature, named position specific scoring matrix combing with physicochemical properties (PSSM-PP), is proposed to represent the conservation information of physicochemical properties of residues. Then the novel feature, orthogonal binary vectors and the secondary structure information are used to establish the RF model for prediction of DNA-binding residues in protein and the prediction classifier achieves 0.6814 Matthew's correlation coefficient (MCC) and 90.23% overall accuracy (ACC) with 77.21% sensitivity (SE) and 91.49% specificity respectively. Further analysis proves that PSSM-PP feature contributes most to the prediction improvement. The results obtained from the comparisons with previous works obviously show that the RF prediction model has successful performance for prediction of DNA-binding residues in novel proteins.

Published in:

Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on

Date of Conference:

10-12 May 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.