Supervised Statistical and Machine Learning Approaches to Inferring Pairwise and Module-Based Protein Interaction Networks

摘要

This paper evaluates three classification techniques: Naive Bayesian (NB), Multilayer Perceptron (MLP) and K-Nearest Neighbour (KNN) that integrate diverse, large-scale functional data to infer pairwise (PW) and module-based (MB) interaction networks in Saccharomyces cerevisiae. Existing multi-source functional data from S. cerevisiae were merged and transformed to construct MB datasets. The results indicate that selection of a classifier depends upon the specific PPI classification problem. Feature integration and encoding methods proposed significantly impact the predictive performance of the classifiers. Generation of PPI maps for S. cerevisiae and beyond will be improved with new, high-quality, large-scale datasets with increased interactome coverage and the integration of classification methods.