{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Urbut SM"],"funding":["NHLBI NIH HHS","NHGRI NIH HHS","NCI NIH HHS"],"pubmed_abstract":["Understanding how disease patterns evolve over a lifetime remains a key challenge in medicine. While electronic health records provide rich longitudinal data, existing models typically analyze each disease in isolation, missing the complex interplay between multiple conditions and genetic factors. Here, we combine longitudinal health records with genetic data to model individual trajectories, using a novel dynamic Bayesian framework called ALADYNOULLI that identifies latent disease signatures from longitudinal health records while modeling individual-specific trajectories. Applied across three biobanks with up to 50 years of follow-up, our model discovers clinically interpretable disease signatures that demonstrate remarkable consistency across diverse populations (79.2% cross-cohort corre"],"journal":["medRxiv : the preprint server for health sciences"],"pagination":["2024.09.29.24314557"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11577253"],"repository":["biostudies-literature"],"pubmed_title":["ALADYNOULLI: A Bayesian approach to disease progression modeling for genomic discovery and clinical prediction."],"pmcid":["PMC11577253"],"funding_grant_id":["R01 HL148050","R01 CA244569","U01 HG011719","R21 HG010748","R01 HL148565","R01 CA227237","T32 HG010464"],"pubmed_authors":["Gaffney L","Misra A","Gusev A","Ding Y","Parmigiani G","Nakao T","Jiang X","Urbut SM","Hornsby W","Smoller JW","Natarajan P"],"additional_accession":[]},"is_claimable":false,"name":"ALADYNOULLI: A Bayesian approach to disease progression modeling for genomic discovery and clinical prediction.","description":"Understanding how disease patterns evolve over a lifetime remains a key challenge in medicine. While electronic health records provide rich longitudinal data, existing models typically analyze each disease in isolation, missing the complex interplay between multiple conditions and genetic factors. Here, we combine longitudinal health records with genetic data to model individual trajectories, using a novel dynamic Bayesian framework called ALADYNOULLI that identifies latent disease signatures from longitudinal health records while modeling individual-specific trajectories. Applied across three biobanks with up to 50 years of follow-up, our model discovers clinically interpretable disease signatures that demonstrate remarkable consistency across diverse populations (79.2% cross-cohort corre","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-14T03:15:12.809Z","creation":"2025-04-04T02:36:23.756Z"},"accession":"S-EPMC11577253","cross_references":{"pubmed":["39568791"],"doi":["10.1101/2024.09.29.24314557"]}}