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.
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