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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
Quantitative assessment of dictionary-based protein named entity tagging
Hongfang Liu1, Zhang-Zhi Hu, Manabu Torii
1Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC 20007, USA. hl224@georgetown.edu
Summary
Biological named entity tagging (BNET) for protein names is complex due to high ambiguity and synonymy. BioThesaurus, a comprehensive gene/protein name resource, demonstrates high coverage of biological literature.
Area of Science:
- Bioinformatics
- Computational Biology
- Natural Language Processing
Background:
- Biological literature mining relies on accurate identification and normalization of gene and protein names.
- Natural language processing (NLP) offers methods for managing and extracting information from scientific texts.
- Biological Named Entity Tagging (BNET) is crucial for linking textual mentions to biological database entries.
Purpose of the Study:
- To quantitatively assess the complexity of BNET for protein entities.
- To evaluate the BioThesaurus resource for gene/protein name normalization.
- To analyze ambiguity, synonymy, and coverage of protein names within a biological thesaurus.
Main Methods:
- Evaluated BioThesaurus using metrics of ambiguity, synonymy, and coverage.
- Assessed name complexity both before and after normalization.
- Utilized the BioCreAtive dataset, comprising MEDLINE abstracts with gene/protein mentions.
Main Results:
- BioThesaurus contains over 2.6 million names, covering 1.8 million UniProtKB entries.
- Average synonymy decreased from 3.53 to 2.86 after normalization.
- Coverage reached 94.0% for gene/protein names in the BioCreAtive dataset, with ambiguity remaining high (around 2.3).
Conclusions:
- Gene and protein names exhibit significant ambiguity and multiple naming conventions.
- The BioThesaurus resource effectively covers the majority of gene/protein names found in biological literature.
- Normalization improves the precision of BNET by reducing synonymy.
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