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Text Mining to Support Gene Ontology Curation and Vice Versa
Patrick Ruch1,2
1SIB Text Mining, Swiss Institute of Bioinformatics, Geneva, Switzerland. patrick.ruch@hesge.ch.
Methods in Molecular Biology (Clifton, N.J.)
|November 5, 2016
Summary
Text mining enhances molecular biology database curation for protein functions. This synergy improves automated Gene Ontology (GO) descriptor assignment, boosting precision and recall by 225% and enabling advanced Question-Answering systems.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Curation of molecular biology databases is crucial for understanding protein functions.
- Existing text mining methods require improvement for accurate Gene Ontology (GO) descriptor assignment.
- The integration of curated data can significantly advance text mining development.
Purpose of the Study:
- To demonstrate how text mining supports the curation of molecular biology databases.
- To highlight the reciprocal relationship between curated data and text mining advancements.
- To present an improved approach for automatic Gene Ontology (GO) descriptor assignment.
Main Methods:
- Review of a decade of efforts in automatic Gene Ontology (GO) descriptor assignment.
- Implementation and performance comparison of an automatic text categorizer.
- Exploration of embedding text categorization into Question-Answering (QA) systems.
Main Results:
- A significant improvement of +225% in both precision and recall for automatic text categorization on benchmarked data.
- Demonstration of the high potential of text mining for enhancing database curation.
- Identification of the need for advanced Deep QA systems trained on curated content.
Conclusions:
- Text mining significantly enhances the accuracy and efficiency of molecular biology database curation.
- High-quality annotated content is essential for the future advancement of text mining tools.
- Database workflows should explicitly record curated and non-curated data to support AI development.
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