Pharmacogenomics
We study how genetic and molecular variation influences drug response, treatment sensitivity, and disease progression. By integrating genomics, transcriptomics, and clinical or experimental drug-response data, we aim to develop predictive and causal models that can help identify therapeutic targets and support precision medicine.
Related Papers
(2023).
DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction.
Quantitative Biology, 11(3), 260-274, 2023.
(2020).
DeepCDR: a hybrid graph convolutional network for predicting cancer drug response.
ECCB Proceedings/Bioinformatics, 36(2), i911-i918, 2020.
(2020).
Reinforced molecular optimization with neighborhood-controlled grammars.
Advances in neural information processing systems (NeurIPS), 33, 8366-8377, 2020.