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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

Disease-related concept mining by knowledge-based two-dimensional gene mapping.

Tsutomu Matsunaga¹, Masaaki Muramatsu

¹Research and Development Headquarters, NTT DATA Corporation, 3-3-9 Toyosu, Koto-ku, Tokyo 135-8671, Japan. matsunagat@nttdata.co.jp

Journal of Bioinformatics and Computational Biology

|October 13, 2007

Summary

A new method, cross-subspace analysis (CSA), organizes biomedical knowledge by plotting over 3,000 human genes. This approach aids in generating hypotheses for disease mechanisms and discovering new scientific insights.

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Area of Science:

Biomedical informatics
Computational biology
Systems biology

Background:

Biomedical knowledge is rapidly expanding, necessitating methods for systematic organization and hypothesis generation.
Current methods lack the ability to automatically structure fragmented knowledge for large-scale integration.

Purpose of the Study:

To introduce cross-subspace analysis (CSA) for organizing and comprehending biomedical knowledge.
To demonstrate CSA's capability in facilitating hypothesis generation and knowledge discovery.

Main Methods:

CSA uses machine learning to analyze occurrence patterns of biomedical terms in MEDLINE abstracts.
Over 3,000 human genes are plotted in a two-dimensional (2D) arrangement based on their functional relationships.
Gene plots sharing common biomedical concepts (e.g., from Gene Ontology) are analyzed to extract relevant concepts.

Main Results:

CSA successfully produced a holistic 2D view of over 3,000 human genes.
Analysis of myocardial infarction and ischemic stroke revealed valid associations with lifestyle, diet-related metabolism, and immune responses.
The method demonstrated its utility in identifying known risk factors for complex diseases.

Conclusions:

Systematic organization of accumulated gene knowledge using CSA can lead to novel hypothesis generation.
CSA enables computational extraction of biomedical concepts, facilitating knowledge discovery across disciplines.
This approach supports understanding complex disease mechanisms and identifying potential risk factors.