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Author

T. Niederhauser

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Open access Jul 2026

An explainable transformer model learning from entire treatment timelines for pan-cancer risk profiling across healthcare systems

Chronicle is introduced, an explainable transformer that learns from entire patient trajectories, predicts diverse clinical outcomes throughout the disease course while capturing both short- and long-term temporal dependencies, providing a scalable framework to support individualized treatment decisions.

P. Keyl, N. Kiermeyer, J. Bosserhoff et al. · 0 citations
Open access Aug 2026

Variantscape: Large Language Model-Driven Mining of Biomedical Literature for Clinical Interpretation of Cancer Variants

Variantscape has the potential to support MTB workflows and translational research by rapidly revealing signals from underlying abstracts, and offers a practical resource for accelerating discovery and supporting precision oncology research and translation.

M. Wosny, A. Blindu, M. Boesch et al. · 0 citations

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