1 code implementation • 30 Sep 2022 • Yulun Wu, Robert A. Barton, Zichen Wang, Vassilis N. Ioannidis, Carlo De Donno, Layne C. Price, Luis F. Voloch, George Karypis
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
2 code implementations • 13 Sep 2022 • Yulun Wu, Layne C. Price, Zichen Wang, Vassilis N. Ioannidis, Robert A. Barton, George Karypis
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
no code implementations • 26 Jul 2022 • Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis Voloch
The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering cancer clinical trials).
no code implementations • 14 May 2022 • Scott Gigante, Varsha G. Raghavan, Amanda M. Robinson, Robert A. Barton, Adeeb H. Rahman, Drausin F. Wulsin, Jacques Banchereau, Noam Solomon, Luis F. Voloch, Fabian J. Theis
Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development.
no code implementations • 12 May 2021 • Robert A. Barton, Tal Neiman, Changhe Yuan
In this case, the problem becomes a classification task on weighted graphs and represents an interesting application area for modern tools such as Graph Neural Networks (GNNs).