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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Ranking relations between diseases, drugs and genes for a curation task
Simon Clematide1, Fabio Rinaldi
1Institute of Computational Linguistics, University of Zurich, Binzmühlestrasse 14, 8050 Zurich, Switzerland. simon.clematide@uzh.ch.
Journal of Biomedical Semantics
|October 11, 2012
Summary
This study introduces a logistic regression method to improve biomedical relation extraction from curated abstracts. The approach significantly enhances the ranking of potential interactions between entities like genes and drugs.
Area of Science:
- Biomedical Informatics
- Natural Language Processing
- Bioinformatics
Background:
- Biomedical text mining aims to extract interactions between entities (genes, proteins, drugs, diseases).
- Existing curated databases like PharmGKB and CTD contain valuable relation information.
- Current text mining systems could better leverage these curated resources for relation extraction.
Purpose of the Study:
- To develop an optimized method for ranking relation candidates from biomedical literature.
- To evaluate the effectiveness of using curated abstracts and metadata for relation extraction.
- To improve the utilization of existing knowledge bases in biomedical text mining.
Main Methods:
- Proposed a logistic regression (maximum entropy modeling) approach for ranking relation candidates.
- Utilized curated abstracts from knowledge bases such as PharmGKB and CTD.
- Examined the impact of metadata (MeSH terms, chemical substance index terms) on relation extraction.
Main Results:
- Achieved significant improvements in ranking quality: 39% (PharmGKB) and 116% (CTD) for AUCiP/R.
- Demonstrated substantial gains for TAP-10 metrics: 53% (PharmGKB) and 134% (CTD) against baseline.
- Validated the method's effectiveness using cross-validation experiments.
Conclusions:
- Leveraging curated relations from knowledge databases strongly enhances relation candidate ranking.
- Concept identification and relation generation benefit from adaptation to previously curated material.
- The method offers a practical approach for extending and re-validating biomedical relations from text.
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