By Topic

Improved method for predicting RNA-binding residues using random forest from primary sequence

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)
Xin Ma ; Golden Audit College, Nanjing Audit University, China ; Yang, Yang

Protein-RNA interactions play important role in a variety of biological processes in cells. An improved method is proposed for predicting RNA-binding residues from amino acids sequences which combines a novel hybrid feature with a random forest (RF) algorithm. The hybrid feature contains the evolutionary information, the secondary structure information and two novel features reflected the information about correlation of amino acids with regards to hydrophobicity and polarity-charge in protein sequences respectively. The prediction classifier achieves 0.5042 Matthew's correlation coefficient (MCC) and 85.17% overall accuracy (ACC) with 52.40% sensitivity (SE) and 92.89% specificity [1] respectively. Further analysis proves that two novel features and the evolutionary information contribute most to the prediction improvement. The results obtained from the comparisons with previous works clearly show that our prediction model has significant better prediction performance of RNA-binding residues in proteins.

Published in:

Information Science and Engineering (ICISE), 2010 2nd International Conference on

Date of Conference:

4-6 Dec. 2010