Using maximum entropy model to extract protein-protein interaction information from biomedical literature

Abstract

Protein-Protein interaction (PPI) information play a vital role in biological research. This work proposes a two-step machine learning based method to extract PPI information from biomedical literature. Both steps use Maximum Entropy (ME) model. The first step is designed to estimate whether a sentence in a literature contains PPI information. The second step is to judge whether each protein pair in a sentence has interaction. Two steps are combined through adding the outputs of the first step to the model of the second step as features. Experiments show the method achieves a total accuracy of 81.9% in BC-PPI corpus and the outputs of the first step can effectively prompt the performance of the PPI information extraction. © Springer-Verlag Berlin Heidelberg 2007.