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

Authors:
Chengjie Sun;Lei Lin;Xiaolong Wang;Yi Guan
Affiliations:
School of Computer Science, Harbin Institute of Technology, Heilongjiang, China;School of Computer Science, Harbin Institute of Technology, Heilongjiang, China;School of Computer Science, Harbin Institute of Technology, Heilongjiang, China;School of Computer Science, Harbin Institute of Technology, Heilongjiang, China
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

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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.