Article

Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks

2024-01-03

Abstract excerpt

Phenotype-driven approaches identify disease-counteracting compounds by analyzing the phenotypic signatures that distinguish diseased from healthy states. Here, we introduce PDGrapher, a causally inspired graph neural network (GNN) model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGra...

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Literature Corpus work: 8046bf2a-9962-5b38-bb97-ffb1df80013a
DOI: 10.1101/2024.01.03.573985

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