Predicting the Regulatory Dynamics of AML Disease Progression from Longitudinal Multi-Modal Clinical Data.

ABSTRACT: Acute Myeloid Leukemia (AML) is a complex and heterogeneous disease identified by severe clinical progression, fast cellular proliferation, and often high mortality rates. Incorporating diverse longitudinal information on patients' medical histories is essential for developing effective disease predictive models applicable to both research and clinical settings. Here, we present a robust methodology for discovering the regulation of disease progression dynamics from a novel longitudinal, multimodal clinical dataset of patients diagnosed with AML. The medical data were analyzed to reveal the main clinical, genetic, and treatment features modulating disease progression. To discover dynamic mathematical models at the systems level-including the necessary regulatory interactions, parameters, a

SUBMITTER: Mousavi R 

PROVIDER: S-EPMC12700996 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature