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Related Experiment Video

Updated: Aug 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

Gilda: biomedical entity text normalization with machine-learned disambiguation as a service.

Benjamin M Gyori¹, Charles Tapley Hoyt¹, Albert Steppi¹

¹Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA.

Bioinformatics Advances

|January 26, 2023

Summary

Gilda is a new tool for matching biomedical concepts like genes and diseases. It uses machine learning to accurately identify terms even when they are ambiguous, improving data integration.

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Area of Science:

Biomedical Informatics
Computational Biology

Background:

Biomedical research relies on accurate identification of entities like genes, proteins, and diseases.
Ambiguity in naming and synonyms across different databases poses a significant challenge for data integration and analysis.

Purpose of the Study:

To develop and present Gilda, a software tool and web service for robust biomedical entity recognition.
To improve the accuracy of matching names and synonyms across diverse biomedical ontologies.

Main Methods:

Implementation of a scored string matching algorithm for biomedical entity recognition.
Integration of machine-learned disambiguation models utilizing contextual information.
Inclusion of species-prioritization functionality to resolve ambiguity.

Main Results:

Gilda effectively matches names and synonyms for genes, proteins, small molecules, biological processes, and diseases.
Machine-learned disambiguation models enhance accuracy by considering surrounding text.
The tool supports species-specific entity resolution.

Conclusions:

Gilda provides a powerful and accurate solution for grounding biomedical concepts.
The software facilitates more reliable data integration and analysis in life sciences.
Gilda is accessible as a web service with open-source code and documentation.