Modelling cellular perturbations using interpretable deep-learning models

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IRISA Rennes
Carl Herrmann (Heidelberg University)

Predicting the effect of drug and genetic perturbations on cells is an important topic with multiple application areas, in particular in cancer research. Deep-learning models can help to learn perturbation patterns using large-scale single-cell datasets and predict combinatorial effects. In addition, interpretable models can highlight perturbed processes, either regarding signaling or transcriptional regulation. I will review recent development in this field and present our own work in this field, with a focus on tumor cell plasticity and interferon response.

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