By Topic

Semi-supervised learning protein complexes from protein interaction 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
$33 $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)
Lei Shi ; Computer Science & Engineering Department, State University of New York at Buffalo, Buffalo, NY 14260, USA ; Aidong Zhang

New technological advances in large-scale protein-protein interaction (PPI) detection provide researchers a valuable source for elucidating the bimolecular mechanism in the cell. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. Many people try to solve the problem by finding dense subgraphs in protein interaction networks with unsupervised methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. First we select some representative topological features and biological features to represent the protein complexes and then utilize the training data to build a multi-layer neural network model and finally detect hidden protein complexes in the protein-protein interaction networks with the obtained model. Experiments show the desirable properties of our proposed algorithm and the effectiveness of our approach.

Published in:

Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on

Date of Conference:

18-21 Dec. 2010