AI/ML & Bioinformatics

Biomarker Analysis

  • Predict patient's response to drug to distinguish responders and non-responders.
  • Use machine learning for clinical trial patient selection and stratification.

Pathway Analysis

  • Overlay gene/protein lists on pathway data sources (GO, KEGG, Panther, MSigDB, etc.) to uncover enriched pathways and hypothesize MOA.
  • Perform network perturbation analysis to identify combination therapy strategies.
biomarker analysis
circular bioinformatics image

Target Identification

  • Learn from diverse data: multi-omics and/or clinical data to identify pathways and networks that lead to new therapeutic targets.
  • Assess target druggability by predicting its structure. 

Drug Repurposing

  • Combine network analysis with ML/AI approaches on integrated biological, pharmacological and clinical data to identify new therapeutic uses for a drug or target.


Artificial Intelligence & Quantitative Systems Pharmacology

  • Identify parameter regimes in quantitative systems pharmacology (QSP) models to generate virtual patients.
  • Use network-based reasoning to inform the structure of QSP models.

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Featured Publication

Two Heads are Better than One: Current Landscape of Integrating QSP and Machine Learning

Published in the Journal of Pharmacokinetics and Pharmacodynamics

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