A Statistical Approach for Extracting Protein-Protein Interactions

Automatic extraction of protein-protein interaction (PPI) information from scientific literature is important for building PPI databases, studying biological networks and discovering new biological knowledge through automatic hypothesis generation. In this paper, we present a new method for PPI extraction based on a mixture of logistic models. The method automatically clusters interaction words (words that describe the interactions of protein pairs) into groups with similar grammatical properties. Logistic models are fitted for each cluster of interaction words. Directionality of interactions is an essential piece of information for many protein interactions and important for building directed biological networks. Most of current PPI extraction methods do not extract the directional information of interactions. This is in part due to the lack of specific corpora with directionality information annotated. We introduce a new corpus, PICAD, for evaluating PPI extraction tools that includes directional annotation. In addition, we propose an ensemble approach using logistic regression, Bayesian Networks, and SVM for identifying PPIs. We show that using an ensemble of classifiers allows us to capture different features in the text and report an F-measure of 75.7% using our new corpus.