LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer. Academic Article uri icon

Overview

abstract

  • 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.

publication date

  • April 6, 2025

Research

keywords

  • Computational Biology
  • Multiomics

Identity

PubMed Central ID

  • PMC11972361

Scopus Document Identifier

  • 105002967153

Digital Object Identifier (DOI)

  • 10.1038/s41467-025-58314-3

PubMed ID

  • 40188173

Additional Document Info

volume

  • 16

issue

  • 1