A graph Kernel for protein-protein interaction extraction

Abstract

In this paper, we propose a graph kernel based approach for the automated extraction of protein-protein interactions (PPI) from scientific literature. In contrast to earlier approaches to PPI extraction, the introduced alldependency- paths kernel has the capability to consider full, general dependency graphs. We evaluate the proposed method across five publicly available PPI corpora providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. Our method is shown to achieve state-of-the art performance with respect to comparable evaluations, achieving 56.4 F-score and 84.8 AUC on the AI med corpus. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect crossvalidation strategies and problems related to comparing F-score results achieved on different evaluation resources.