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Approximate Subgraph Matching-Based Literature Mining for Biomedical Events and Relations
- Source :
- PLoS ONE, PLoS ONE, Vol 8, Iss 4, p e60954 (2013)
- Publication Year :
- 2013
- Publisher :
- Public Library of Science, 2013.
-
Abstract
- The biomedical text mining community has focused on developing techniques to automatically extract important relations between biological components and semantic events involving genes or proteins from literature. In this paper, we propose a novel approach for mining relations and events in the biomedical literature using approximate subgraph matching. Extraction of such knowledge is performed by searching for an approximate subgraph isomorphism between key contextual dependencies and input sentence graphs. Our approach significantly increases the chance of retrieving relations or events encoded within complex dependency contexts by introducing error tolerance into the graph matching process, while maintaining the extraction precision at a high level. When evaluated on practical tasks, it achieves a 51.12% F-score in extracting nine types of biological events on the GE task of the BioNLP-ST 2011 and an 84.22% F-score in detecting protein-residue associations. The performance is comparable to the reported systems across these tasks, and thus demonstrates the generalizability of our proposed approach.
- Subjects :
- Dependency (UML)
Matching (graph theory)
Process (engineering)
Computer science
Text Mining
Science
Subgraph isomorphism problem
Biomedical Technology
Information Storage and Retrieval
Biological Data Management
02 engineering and technology
computer.software_genre
Bioinformatics
Social and Behavioral Sciences
03 medical and health sciences
Engineering
0202 electrical engineering, electronic engineering, information engineering
Data Mining
Amino Acids
Databases, Protein
Biology
Information Science
030304 developmental biology
Natural Language Processing
0303 health sciences
Multidisciplinary
Parsing
Publications
Computational Biology
Proteins
Reproducibility of Results
Biomedical text mining
Graph
Kernel method
Computer Science
Signal Processing
Medicine
020201 artificial intelligence & image processing
Data mining
Information Technology
computer
Sentence
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 8
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....175e80979ed55b946ff2ab26bde11fe6