Computational prediction of protein interaction networks through supervised classification techniques

Fiona Browne, Haiying Wang*, Huiru Zheng, Francisco Azuaje

*Corresponding author for this work

    Research output: Contribution to journalArticleResearchpeer-review


    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
    Number of pages17
    JournalInternational Journal of Functional Informatics and Personalised Medicine
    Issue number2
    Publication statusPublished - 2008


    • PPIs
    • Protein-Protein Interactions
    • computational systems biology
    • dataset integration
    • feature encoding
    • functional data
    • machine and statistical learning
    • module-based interactions


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