Paper Info
Title
A Hybrid Protein-Protein Interaction Triple Extraction Method For Biomedical Literature
Abstract
Protein-protein interaction extraction research can be widely applied to the field of life science research. However, most of the machine learning based methods focus on binary PPI relation extraction, which loses rich relationship type information that is critical to the PPIs study. The rule based open information extraction methods can extract the PPI triple (i.e. "protein1, interaction word, protein2"), but suffers from low recall rate problem. In this paper, we propose a hybrid protein-protein interaction triple extraction method. In this method, firstly, machine learning techniques are used to recognize protein entities and extract relational protein pairs. Then, the syntactic patterns and a dictionary are employed to find out corresponding interaction words that represent the relationships between two proteins. This method obtains an F-score of 40.18% on the AImed corpus, which is much higher than the result achieved by the rule based Stanford open information extraction method.
Year
DOI
Venue
2017
10.1109/BIBM.2017.8217886
2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
DocType
ISSN
protein protein interaction triple extraction, interaction word extraction, protein named entity recognition
Conference
2156-1125
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Zhehuan Zhao102.70
Zhihao Yang27315.35
Cong Sun372.15
Lei Wang45613.90
Hongfei Lin5768122.52