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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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Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
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Related Experiment Video

Updated: May 14, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction.

Xinmeng Zhang1, Eugene Jeong2, Chao Yan3

  • 1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.

Biorxiv : the Preprint Server for Biology
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

CellAwareGNN, a novel graph foundation model, enhances drug repurposing by integrating cell-type-specific genomics into biomedical knowledge graphs. This approach improves prediction accuracy for drug-disease indications, especially for autoimmune diseases.

Keywords:
AI for ScienceAutoimmune DiseasesDrug RepurposingGraph Foundation ModelsKnowledge GraphsSingle-Cell Genomics

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Related Experiment Videos

Last Updated: May 14, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Computational biology and bioinformatics
  • Drug discovery and development
  • Genomics and precision medicine

Background:

  • Graph foundation models show promise for drug repurposing using biomedical knowledge graphs.
  • Existing models like TxGNN lack fine-grained, cell-type-specific genomic context, limiting their ability to model cell-specific disease mechanisms.
  • Previous evaluations often used limited disease subsets, hindering comprehensive performance assessment.

Purpose of the Study:

  • To develop a graph foundation model, CellAwareGNN, that integrates single-cell genomics for improved drug repurposing.
  • To construct an enhanced knowledge graph, scPrimeKG, incorporating cell-type-specific genetic associations.
  • To evaluate CellAwareGNN's performance across all diseases, focusing on predictive accuracy and biological interpretability.

Main Methods:

  • Updated PrimeKG to PrimeKG-U with expanded biomedical knowledge.
  • Developed TxGNN-U as an improved baseline model.
  • Constructed scPrimeKG by integrating single-cell genomics data (OneK1K) into PrimeKG-U, increasing graph size.
  • Pre-trained CellAwareGNN on scPrimeKG and evaluated its drug indication prediction capabilities.

Main Results:

  • CellAwareGNN consistently outperformed TxGNN and TxGNN-U in drug indication prediction, achieving an AUPRC of 0.826.
  • Significant improvements were observed for autoimmune diseases, with CellAwareGNN reaching an AUPRC of 0.864.
  • CellAwareGNN identified promising drug repurposing candidates, such as Ocrelizumab for Pemphigus, with cell-type-specific rationales.

Conclusions:

  • Integrating cell-type-specific genomic context into graph foundation models significantly enhances drug repurposing.
  • CellAwareGNN demonstrates superior predictive performance and biological interpretability compared to existing methods.
  • The findings highlight the value of single-cell genomics for advancing precision medicine and drug discovery.