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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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MecCog: a knowledge representation framework for genetic disease mechanism.
Kunal Kundu1,2, Lindley Darden3, John Moult2,4
1Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, MD 20742, USA.
Bioinformatics (Oxford, England)
|June 12, 2021
Summary
MecCog is a graphical framework for integrating genetic disease mechanisms. This tool aids in identifying biomarkers and therapeutic targets by visualizing disease pathways.
Area of Science:
- Genetics
- Bioinformatics
- Systems Biology
Background:
- Genetic disease mechanisms are fragmented across scientific literature.
- Current representations (text, diagrams) hinder integration and utility.
Purpose of the Study:
- To present MecCog, a graphical framework for constructing integrated representations of genetic disease mechanisms.
- To facilitate the organization and visualization of how genetic variants lead to disease phenotypes.
Main Methods:
- Developed MecCog, a web platform for creating 'mechanism schemas'.
- Schemas depict perturbation propagation across biological organization levels.
- Utilizes graphical notations, hyperlinked evidence, ontology, and uncertainty depiction.
Main Results:
- MecCog enables users to build, store, publish, and query mechanism schemas.
- Schemas visualize disease mechanisms, aiding in identifying biomarkers and therapeutic targets.
- Facilitates the identification of critical future experiments.
Conclusions:
- MecCog provides a structured approach to representing complex genetic disease mechanisms.
- The framework enhances the utility of experimental findings for research and therapeutic development.
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