Computational prediction of protein interaction networks through supervised classification techniques

Browne Fiona, HY Wang, H Zheng, Azuaje Francisco

Research output: Contribution to journalArticle

Abstract

This paper implements integrative methods to predict Pairwise (PW) and Module-Based (MB) protein interactions in Saccharomyces cerevisiae. The predictive ability of combining diverse sets of relatively strong and weak predictive datasets is investigated. Different classification techniques: Naive Bayesian (NB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. The assessment demonstrated that as the predictive power of single-source datasets became weaker, MLP and NB performed better than KNN. Generation of PPI maps for S. cerevisiae and beyond will be improved with new, higher-quality datasets with increased interactome coverage and the integration of classification methods.
Original languageEnglish
Pages (from-to)205-221
JournalInternational Journal of Functional Informatics and Personalised Medicine
Volume1
Issue number2
DOIs
Publication statusPublished - Sep 2008

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