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Large-scale extraction of gene interactions from full-text literature using DeepDive
Emily K Mallory1, Ce Zhang2, Christopher Ré3
1Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA.
Bioinformatics (Oxford, England)
|September 5, 2015
Summary
This study used DeepDive to extract gene-gene interactions, including protein-protein and transcription factor interactions, from over 100,000 scientific articles. The system achieved 76% precision and 49% recall, identifying 3356 unique gene pairs.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Understanding gene-gene interactions is crucial for cellular processes, disease, and drug response.
- Biomedical literature is a primary source for known interactions, but manual curation is challenging due to exponential growth.
- Existing databases like BioGRID and ChEA face scalability issues with increasing literature volume.
Purpose of the Study:
- To develop and evaluate a system using DeepDive for automated extraction of gene-gene interactions from full-text scientific articles.
- To extract both protein-protein interactions and transcription factor interactions.
- To assess the system's performance against established databases and random curation.
Main Methods:
- Developed a DeepDive-based extractor to identify candidate gene-gene relations within sentences.
- Assigned a probability score to each candidate relation indicating the likelihood of a correct interaction.
- Evaluated the system using the Database of Interacting Proteins and randomly curated extractions.
Main Results:
- Achieved 76% precision and 49% recall in extracting direct and indirect gene interactions from sentences.
- Demonstrated precision ranging from 62% to 83% for random extractions, considering different precision metrics.
- Successfully extracted 3356 unique gene pairs utilizing 724 features from over 100,000 PLOS articles.
Conclusions:
- DeepDive provides an effective method for large-scale, automated extraction of gene-gene interactions from biomedical literature.
- The developed system demonstrates competitive performance in identifying protein-protein and transcription factor interactions.
- The source code is publicly available, facilitating further research and application in genomics and drug discovery.
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