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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Using ontology-based annotation to profile disease research.

Yi Liu¹, Adrien Coulet, Paea LePendu

¹Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California 94305, USA. liuyipei@stanford.edu

Journal of the American Medical Informatics Association : JAMIA

|April 13, 2012

Summary

This summary is machine-generated.

This study uses disease ontology (DO) to analyze research funding and publication trends. DO-based annotation effectively profiles biomedical research activity and identifies leaders in specific disease areas.

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Area of Science:

Biomedical research
Health sciences
Research informatics

Background:

Profiling research activity is crucial but hindered by a lack of common classification systems.
Ontology-based annotation offers a solution for systematic research activity profiling.
Analyzing funding and publication data provides insights into research trends.

Purpose of the Study:

To introduce and explore ontology-based annotation for profiling research activity.
To analyze trends in disease research across topics, institutions, and time.
To evaluate the utility of classification hierarchies using a use-case driven mechanism.

Main Methods:

Ontology-based annotation using disease ontology (DO).
Analysis of over a decade of research funding and publication data.
Clustering of disease topic funding profiles based on ontology hierarchy.

Main Results:

Identified leaders in research activity within specific disease areas (e.g., high mortality/sponsorship).
Funding profiles of disease topics align with ontology hierarchy and funding agency priorities.
Discovered four distinct temporal trends in research topics.

Conclusions:

Disease ontology (DO)-based annotation effectively profiles the biomedical research landscape.
The study proposes a mechanism to evaluate the utility of classification hierarchies.
Ontology-based methods enhance the systematic analysis of research trends and funding.