Discovering patterns to extract protein-protein interactions from full biomedical texts

Authors:
Minlie Huang;Xiaoyan Zhu;Donald G. Payan;Kunbin Qu;Ming Li
Affiliations:
University of Tsinghua, Beijing, China;University of Tsinghua, Beijing, China;Rigel Pharmaceuticals Inc., South San Francisco, CA;Rigel Pharmaceuticals Inc., South San Francisco, CA;University of Waterloo, Canada and University of Tsinghua, Beijing, China
Venue:
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Year:
2004

Citing 2
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Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Computational Linguistics
PathwayFinder: paving the way towards automatic pathway extraction

APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29

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Abstract

Although there have been many research projects to extract protein pathways, most such information still exists only in the scientific literature, usually written in natural languages and defying data mining efforts. We present a novel and robust approach for extracting protein-protein interactions from the literature. Our method uses a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions. A matching algorithm is designed to extract the interactions between proteins. Equipped only with a protein name dictionary, our system achieves a recall rate of about 80.0% and a precision rate of about 80.5%.