A recent report in Nature outlines how a team, led by Xiong Weichuan, used machine learning to identify 11 immune-related biomarkers in sepsis. The study combines genomic analysis and immune checkpoint evaluation to offer actionable insights for early detection and tailored immunotherapy. This research could inspire improved clinical practices.

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Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets