By Topic

Semi-supervised Method for Extraction of Protein-Protein Interactions Using Hybrid Model

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

3 Author(s)
Weizhong Qian ; Comput. Sci. & Eng. Sch., Univ. of Electron. Sci. & Technol. of China, Chengdu, China ; Chong Fu ; Hongrong Cheng

The poor performance and the lack of manual labeled corpus are two main problems in the task of protein-protein interaction extraction. A novel hybrid method is proposed. Based on the individual characteristics of machine learning and pattern learning, this method utilizes learned patterns from pattern learning to generate pattern features by performing sequence alignment. The pattern features and word features are incorporated into the input feature set of machine learning algorithms. The semi-supervised method based on k-nearest neighbours classifier is also proposed to train the hybrid method from unlabeled data automatically. Experimental results show the improved performance over the baseline methods with the hybrid model and the efficieny of the semi-supervised method for the lack of labeled data.

Published in:

Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on

Date of Conference:

16-18 Jan. 2013