Article

Interventionally-guided representation learning for robust and interpretable AI models in cancer medicine

2025-07-21

Abstract excerpt

Machine learning models hold promise in cancer medicine but often lack robustness and interpretability. We introduce a new class of model for high-dimensional molecular data that incorporate interventional auxiliary information to learn latent representations that are informative and interpretable by design. By using causal signals from genetic loss-of-function screens, our approach generates representations that...

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Identifiers and source

Literature Corpus work: 93b654d3-b336-59dd-915b-573fb1c4c0c3
DOI: 10.1101/2025.07.21.662350

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