Article

Interpretable Forecasting of Kidney Cancer Progression via Generative AI and Symbolic Reasoning

2026-08-26

Abstract excerpt

Predicting cancer stage progression from omics data, and deriving molecular insight into the mechanisms driving it, remains a major challenge, owing in part to the lack of adequate longitudinal data and the interpretability limitations of current forecasting models. Large cancer datasets such as TCGA capture patient profiles cross-sectionally rather than longitudinally, complicating timely treatment decisions as t...

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Literature Corpus work: 872af514-1e05-585f-9b22-48faeeb16a2c
DOI: 10.64898/2026.08.23.746526

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