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Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Related Experiment Video

Updated: Jun 21, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Human disease-drug network based on genomic expression profiles.

Guanghui Hu1, Pankaj Agarwal

  • 1Computational Biology, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America. guanghui.2.hu@gsk.com

Plos One
|August 7, 2009
PubMed
Summary

This study created a large disease-drug network from gene expression data to identify new uses for existing drugs and potential drug targets. The findings facilitate drug repositioning and accelerate the discovery of novel therapeutic strategies.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Pharmacology

Background:

  • Drug repositioning accelerates drug discovery by reducing risks and development times.
  • High-throughput technologies and genomic datasets enable comprehensive characterization of diseases and drugs.
  • Gene expression, protein, metabolite, and phenotype data are key for understanding disease and drug interactions.

Purpose of the Study:

  • To construct a large-scale disease-drug network using genomic expression profiles.
  • To identify novel therapeutic indications for existing drugs through systematic analysis.
  • To discover potential drug targets and pathways for various conditions.

Main Methods:

  • Performed a systematic analysis of genomic expression profiles for human diseases and drugs.
  • Extracted a network of 170,027 significant interactions from ~24.5 million comparisons.
  • Utilized publicly available transcriptomic profiles from approximately 7,000 datasets.

Main Results:

  • Constructed a network including 645 disease-disease, 5,008 disease-drug, and 164,374 drug-drug relationships.
  • Identified potential new drug indications, such as antimalarials for Crohn's disease.
  • Aided in drug side effect identification and elucidated drug targets/pathways, e.g., KCNMA1 for lobeline.

Conclusions:

  • Generated thousands of disease and drug expression profiles automatically from GEO datasets.
  • Constructed a comprehensive disease-drug network for efficient drug repositioning.
  • Enabled effective drug target and pathway identification through network analysis.