Search Results for author: Robert A. Barton

Found 5 papers, 2 papers with code

Variational Causal Inference

2 code implementations13 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.

Causal Inference counterfactual

Neural Design for Genetic Perturbation Experiments

no code implementations26 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).

SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching

no code implementations14 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.

Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution

no code implementations12 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).

Entity Resolution Graph Classification +1

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