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  • LEOPARD [Elektronski vir] : missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer
    Han, Siyu ...
    Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the ... common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on four real-world omics datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare.
    Vir: Nature communications [Elektronski vir]. - ISSN 2041-1723 (Vol. 16, no. 1, [Article no.] 3278, 2025, str. 1-20)
    Vrsta gradiva - e-članek ; neleposlovje za odrasle
    Leto - 2025
    Jezik - angleški
    COBISS.SI-ID - 264903171

    Povezava(-e):

    https://www.nature.com/articles/s41467-025-58314-3
    DOI

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