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This paper introduces a stochastic causal representation learning framework to resolve the bias-precision paradox in personalized medicine, demonstrating improved accuracy and interpretability in ICU clinical decision support.
GitLab co-founder Sid Sijbrandij open-sourced 25 TB of multi-omic tumor data and used ChatGPT-driven agents to design custom therapies, including a FAP-targeted radioligand and an mRNA vaccine, for his relapsed osteosarcoma.