By Topic

Independent component analysis-based prediction of O-Linked glycosylation sites in protein using multi-layered neural networks

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)
Chu-Zheng Wang ; Coll. of Comput. & Inf. Eng, Central South Univ. of Forestry & Technol., Changsha, China ; Xiao-Feng Tan ; Yen-wei Chen ; Xian-Hua Han
more authors

In this paper, we develop a new method for prediction O-linked glycosylation site and pattern analysis in protein, which combines independent component analysis (ICA) with a multi-layer neural network (NN). ICA is first used to construct main basis (subspace) of the protein sequence for features extraction. The projections of protein sequence on the subspace with low dimension are used as input data instead of the higher-dimensional protein sequences. Neural network is built to predict whether a particular site of serine or threonine is glycosylated. Compared with other subspace method, our proposed new method can improve the prediction accuracy.

Published in:

Signal Processing (ICSP), 2010 IEEE 10th International Conference on

Date of Conference:

24-28 Oct. 2010