Predicting types of protein-protein interactions using a multiple-instance learning model

We propose a method for predicting types of protein-protein interactions using a multiple-instance learning (MIL) model. Given an interaction type to be predicted, the MIL model was trained using interaction data collected from biological pathways, where positive bags were constructed from interactions between protein complexes of that type, and negative bags from those of other types. In an experiment using the KEGG pathways and the Gene Ontology, the method successfully predicted an interaction type (phosphorylation) to an accuracy rate of 86.1%.