ArticleInternational journal of genomics2018
Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine.
Article in International journal of genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Review
- Computer-Aided Drug Design in Research on Chinese Materia Medica: Methods, Applications, Advantages, and Challenges.Pharmaceutics · 2025Review
- Exploring the bioactive properties and mechanism ofAmerican journal of translational research · 2025Article
- Current trends and future prospects of drug repositioning in gastrointestinal oncology.Frontiers in pharmacology · 2023Review
- In Silico and In Vivo Studies on the Mechanisms of Chinese Medicine Formula (Gegen Qinlian Decoction) in the Treatment of Ulcerative Colitis.Frontiers in pharmacology · 2021Article
- Overcoming cancer therapeutic bottleneck by drug repurposing.Signal transduction and targeted therapy · 2020Review
- Systems biology approaches in the study of Chinese herbal formulae.Chinese medicine · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with
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Registered trials
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