Title: Computational prediction of protein interaction networks through supervised classification techniques
Authors: Fiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje
Addresses: School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland, UK. ' School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland, UK. ' School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland, UK. ' Research Centre for Public Health (CRP-Sante) 1AB rue Thomas Edison, Strassen L-1445, Luxembourg
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.
Keywords: protein-protein interactions; PPI prediction; module-based interactions; machine learning; statistical learning; functional data; feature encoding; dataset integration; computational systems biology; protein interaction networks; supervised classification; Saccharomyces cerevisiae.
International Journal of Functional Informatics and Personalised Medicine, 2008 Vol.1 No.2, pp.205 - 221
Available online: 08 Sep 2008 *Full-text access for editors Access for subscribers Free access Comment on this article