20.500.12556/RUL-181916 LEOPARD missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer 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. multi-timepoint omics data representation temporal knowledge transfer veččasovni omični podatki reprezentacija časovni prenos znanja true false true Angleški jezik Slovenski jezik Članek v reviji 2026-04-20 08:28:25 2026-04-20 08:28:35 2026-07-17 15:56:46 0000-00-00 00:00:00 2025 0 0 20 str. art. 3278 Vol. 16 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 577:61 2041-1723 10.1038/s41467-025-58314-3 264903171 RAZ_Han_Siyu_2025.pdf RAZ_Han_Siyu_2025.pdf 1 F1F42E32906033B3985CDD71A8A0F407 56abac61c3260f5525d4d83f98b02793c89b987a85044607f496e3b7fda90b50 67f3bb93-3e0d-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=232862 https://www.nature.com/articles/s41467-025-58314-3 1 1d5b80a1-81e1-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=240393 Medicinska fakulteta 0 0 0