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BELTracker: evidence sentence retrieval for BEL statements
Majid Rastegar-Mojarad1, Ravikumar Komandur Elayavilli2, Hongfang Liu2
1Department of Health Sciences Research, Mayo Clinic, USA University of Wisconsin-Milwaukee, Milwaukee, WI, USA mojarad.majid@mayo.edu.
Summary
BELTracker aids in curating biological networks by retrieving and ranking evidence sentences from literature for Biological Expression Language (BEL) statements. This tool enhances biological network representation and validation from scientific publications.
Area of Science:
- Bioinformatics
- Computational Biology
- Literature Mining
Background:
- Biological Expression Language (BEL) is crucial for representing biological networks.
- Literature is the primary source for BEL curation, but identifying relevant evidence is challenging.
Purpose of the Study:
- To develop and evaluate BELTracker, a tool for retrieving and ranking evidence sentences from PubMed for BEL statements.
- To assist in the curation and validation of biological networks represented in BEL.
Main Methods:
- BEL statements are translated into Information Retrieval (IR) queries.
- PubMed citations are retrieved based on these queries.
- Evidence sentences are identified and ranked using a combination of IR, machine learning, and heuristics.
Main Results:
- BELTracker successfully identified relevant evidence sentences for 72 out of 97 BEL statements within the top 10 retrieved sentences.
- The system achieved precisions of 0.392 (full), 0.532 (relaxed), and 0.615 (context).
Conclusions:
- BELTracker effectively retrieves and ranks evidence from literature to support BEL statement curation.
- The tool is available as a public RESTful API, facilitating broader use in biological network analysis.
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