Work

Aladynoulli: a Bayesian framework for longitudinal EHR and genetic discovery — code and supplementary data (v1.0)

Year: 2026

Type: software

Abstract: Versioned snapshot of code, reviewer-response notebooks, and supplementary data files (RVAS summary statistics, exported model parameters for UK Biobank / Mass General Brigham / All of Us, Cohen's d tables, GWAS annotations, signature lead-SNP tables) accompanying the Nature manuscript "Aladynoulli: a Bayesian framework for longitudinal EHR and genetic discovery" by Urbut SM, Ding Y, Nakao T, Koyama S, Jiang X, Harish A, Gaffney L, Hornsby W, Smoller JW, Gusev A, Natarajan P, Parmigiani G. Latest updated public code mirror: https://github.com/surbut/aladynoulli2.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20187988

Authors: Urbut, Sarah, Gusev, Alexander, Natarajan, Pradeep, Parmigiani, Giovanni

Institutions: Dana-Farber Cancer Institute, Broad Institute, Harvard University, Massachusetts General Hospital

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7161169021

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