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Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation.

A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic