A framework using autoencoders quantifies patient-level similarity to development data and measures predictive model performance across similarity subgroups to distinguish case-mix effects from model deficiencies in external validation.
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Theoretical frameworks are outlined that tie phase-separation propensity, material states (liquids, gels, aggregates), and viscoelastic properties of biomolecular condensates to intermolecular interaction strength, valency, and bond lifetimes.
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Rethinking external validation for the target population: Capturing patient-level similarity with a generative model
A framework using autoencoders quantifies patient-level similarity to development data and measures predictive model performance across similarity subgroups to distinguish case-mix effects from model deficiencies in external validation.
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Determinants of Phase-Separation Propensities, Material States, and Material Properties of Biomolecular Condensates
Theoretical frameworks are outlined that tie phase-separation propensity, material states (liquids, gels, aggregates), and viscoelastic properties of biomolecular condensates to intermolecular interaction strength, valency, and bond lifetimes.