Public articles linked to the same research event.
Clinical and molecular hepatology This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec
This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec
This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec
This review organizes current work toward an AI Virtual Cell (AIVC) for the liver into three complementary modeling routes—generative models that represent cell states, dynamics and transport models that infer state transitions, and pretrained or foundation models that test whether learned representations transfer across donors, etiologies, disease stages, and platforms—with perturbation-response prediction as a cross-cutting assessment, concluding that published models demonstrate only individual components such as atlas integration, inferred trajectories, transferable representations, and retrospective response programs, and do not yet constitute a prospectively validated liver simulator, so near-term use should prioritize experiment selection and hypothesis generation while clinical dec