A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.

ABSTRACT: Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC12705450 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature