LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

Han, Siyu ; Yu, Shixiang; Shi, Mengya; Harada, Makoto; Ge, Jianhong; Lin, Jiesheng; Prehn, Cornelia ; Petrera, Agnese; Li, Ying; Sam, Flora ; Matullo, Giuseppe; Adamski, Jerzy ; Suhre, Karsten ; Gieger, Christian ; Hauck, Stefanie M. ; Herder, Christian; Roden, Michael ; Casale, Francesco Paolo ; Cai, Na; Peters, Annette und Wang-Sattler, Rui (2025): LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer. In: Nature Communications, Bd. 16, 3278

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.

• Dokumententyp: Zeitschriftenartikel
• EU Funded Grant Agreement Number: 821508
• Fakultät: Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 2041-1723
• Sprache: Englisch