PerturbRx
Lead research directionA framework for learning intervention-induced biological state transitions from perturbational data and transferring those dynamics toward patient-level drug-response prediction.
Research portfolio
Selected projects
My work centers on learning how biological systems respond to therapeutic interventions, and on transferring those models across cellular, molecular, and patient contexts.
Featured research
Core research program
drGT
Co-developed experiments · Led analysisA heterogeneous graph model connecting drugs, cell lines, and genes to predict response while producing gene-level, mechanism-oriented attributions.
DrugAgent
First author · Framework developmentA multi-agent framework for integrating heterogeneous and conflicting biomedical evidence for drug-target interaction prediction and therapeutic discovery.
Earlier & collaborative work
Methods, resources, and translational studies
GraphPINE
First author · Method developmentGraph importance propagation for interpretable drug-response prediction, incorporating prior biological relationships into feature importance learning.
BiGCN
First author · Method developmentA bi-graph convolutional approach leveraging cell and gene similarities for single-cell transcriptome imputation.
CellMinerCDB 2.2
Collaborative researchA cross-database platform for exploring pharmacogenomic relationships across patient-derived cancer cell-line datasets.
TRPM4 and Acetalax
Collaborative researchInvestigation of TRPM4 as a predictive biomarker and mechanistic driver of Acetalax activity in prostate cancer.
Resources
Open research infrastructure
Research Toolbox
Open-source developmentReusable utilities for evaluation, reproducibility, molecular fingerprints, biomedical identifier mapping, caching, PMC XML processing, and scientific visualization.
Research Ideas
Public research notebookExploratory questions and forward-looking directions in therapeutic-response modeling, biological dynamics, and biomedical AI.