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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
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Learning the Structure of Biomedical Relationships from Unstructured Text.
Bethany Percha1, Russ B Altman2
1Biomedical Informatics Training Program, Stanford University, Stanford, California, United States of America.
Plos Computational Biology
|July 29, 2015
Summary
A new text mining algorithm, Ensemble Biclustering for Classification (EBC), automatically extracts drug-gene relationships from biomedical literature. This method overcomes variations in language to discover new connections and understand how findings are presented.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Natural Language Processing
Background:
- Biomedical research findings on drug-gene interactions are vast but unstructured in millions of articles.
- Structured data is crucial for identifying drug response biomarkers and predicting drug-drug interactions.
- Current text mining methods struggle with the variability of language used to describe similar biological relationships.
Purpose of the Study:
- To develop a novel text mining algorithm, Ensemble Biclustering for Classification (EBC), for extracting drug-gene relationships from unstructured biomedical text.
- To validate the EBC algorithm's performance against established pharmacogenomic and drug-target databases.
- To apply EBC to the entire Medline database to map the universe of drug-gene relationships and uncover novel patterns.
Main Methods:
- Developed the Ensemble Biclustering for Classification (EBC) algorithm, a novel approach for automated relationship extraction from text.
- Validated EBC using manually-curated pharmacogenomic data from PharmGKB and drug-target data from DrugBank.
- Applied EBC to Medline to analyze the structure of drug-gene relationships described in scientific literature.
Main Results:
- EBC successfully extracts drug-gene relationships, overcoming differences in word choice and sentence structure.
- Validation against PharmGKB and DrugBank confirmed EBC's accuracy and utility in discovering new relationships.
- Analysis of Medline revealed unexpected structural patterns in how drug-gene relationships are described, differentiating newer findings from established knowledge.
Conclusions:
- The EBC algorithm provides a flexible and adaptable method for large-scale biomedical text mining.
- EBC facilitates the creation of structured resources, accelerating genomic biomarker discovery and drug-interaction prediction.
- The study highlights novel insights into the linguistic representation of scientific findings regarding drug-gene interactions.
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