By Topic

Supervised Statistical and Machine Learning Approaches to Inferring Pairwise and Module-Based 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
$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

4 Author(s)
Browne, F. ; Univ. of Ulster at Jordanstown, Newtownabbey ; Haiying Wang ; Huiru Zheng ; Azuaje, F.

This paper evaluates three classification techniques: Naive Bayesian (NB), multilayer perceptron (MLP) and K-nearest neighbour (KNN) that integrate diverse, large-scale functional data to infer pairwise (PW) and module-based (MB) interaction networks in Saccharomyces cerevisiae. Existing multi-source functional data from S. cerevisiae were merged and transformed to construct MB datasets. The results indicate that selection of a classifier depends upon the specific PPI classification problem. Feature integration and encoding methods proposed significantly impact the predictive performance of the classifiers. Generation of PPI maps for S. cerevisiae and beyond will be improved with new, high-quality, large-scale datasets with increased interactome coverage and the integration of classification methods.

Published in:

Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on

Date of Conference:

14-17 Oct. 2007