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Related Experiment Video

Updated: Jan 28, 2026

Assessing Changes in Volatile General Anesthetic Sensitivity of Mice after Local or Systemic Pharmacological Intervention
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Omics Data Integration and Analysis for Systems Pharmacology.

Hansaim Lim1, Lei Xie2,3

  • 1The Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, New York, NY, USA.

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|March 9, 2019
PubMed
Summary

Systems pharmacology uses machine learning to predict drug-target interactions. This study details methods for drug-target association prediction using the REMAP tool, applicable to network-based drug design.

Keywords:
Big dataCollaborative filteringDrug repurposingMachine learningOff-target identification

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Bioinformatics

Background:

  • Systems pharmacology integrates multi-scale data for drug action understanding.
  • Machine learning is crucial for data-driven drug discovery and development.
  • Integrating multiple omics data is a key step in systems pharmacology.

Purpose of the Study:

  • To describe procedures for drug-target association prediction.
  • To introduce the REMAP tool for large-scale off-target prediction.
  • To demonstrate applicability to network-based drug design.

Main Methods:

  • Utilizing the REMAP tool for large-scale off-target prediction.
  • Integrating multiple omics data for analysis.
  • Applying machine learning for optimization and prediction.

Main Results:

  • Detailed procedures for drug-target association prediction are presented.
  • The REMAP tool facilitates large-scale off-target predictions.
  • The described method is adaptable for various relation inference tasks.

Conclusions:

  • The REMAP tool and associated methods support network-based drug design.
  • This approach enhances understanding of drug actions within biological networks.
  • The methodology is valuable for advancing systems pharmacology research.