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The evolution of knowledge on genes associated with human diseases
Thomaz Lüscher-Dias1, Rodrigo Juliani Siqueira Dalmolin2,3, Paulo de Paiva Amaral4
1Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
Iscience
|January 10, 2022
Summary
This study used text mining to build a gene-disease knowledge network, revealing disease mechanisms and drug targets over 30 years. The findings offer insights into shared molecular pathways across distinct disorders.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Genomics
Background:
- Thousands of biomedical articles are published weekly, containing information on gene-disease associations.
- Automated detection of gene-disease links is crucial for advancing biomedical research.
Purpose of the Study:
- To construct and analyze a gene-disease knowledge network using text mining.
- To track the evolution of knowledge regarding gene-disease associations over 30 years.
- To uncover molecular bases of diseases and shared mechanisms between distinct diseases.
Main Methods:
- Utilized a cognitive computing text-mining application.
- Constructed a knowledge network of 3,723 genes and 99 diseases.
- Analyzed yearly changes in the network to assess knowledge evolution.
Main Results:
- The study successfully mapped gene-disease relationships and their temporal changes.
- Identified shared molecular mechanisms underlying clinically distinct diseases.
- Revealed that multi-purpose drugs target genes associated with psychiatric, inflammatory, or infectious disorders.
Conclusions:
- Text mining provides a powerful systems approach to navigate vast biomedical literature.
- The constructed knowledge network offers insights into disease mechanisms and potential therapeutic targets.
- Understanding gene-disease networks aids in identifying common pathways and drug repurposing opportunities.
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