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Computational prediction of protein interaction networks through supervised classification techniques

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Fiona, Browne, Wang, HY, Zheng, H and Francisco, Azuaje (2008) Computational prediction of protein interaction networks through supervised classification techniques. International Journal of Functional Informatics and Personalised Medicine (IJFIPM), 1 (2). pp. 205-221. [Journal article]

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DOI: 10.1504/IJFIPM.2008.020188

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.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:8836
Deposited By:Dr Haiying Wang
Deposited On:20 Jan 2010 16:23
Last Modified:20 Jan 2010 16:23

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