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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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
|Deposited By:||Dr Haiying Wang|
|Deposited On:||20 Jan 2010 16:23|
|Last Modified:||09 May 2016 10:55|
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