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Han, S. ; Yu, S. ; Shi, M. ; Harada, M. ; Ge, J. ; Lin, J. ; Prehn, C. ; Petrera, A. ; Li, Y.* ; Sam, F.* ; Matullo, G.* ; Adamski, J. ; Suhre, K.* ; Gieger, C. ; Hauck, S.M. ; Herder, C.* ; Roden, M.* ; Casale, F.P. ; Cai, N. ; Peters, A. ; Wang-Sattler, R.

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

Nat. Commun. 16:3278 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 16, Heft: 1, Seiten: , Artikelnummer: 3278 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Translational Genomics (ITG)
Institute of Epidemiology (EPI)
CF Metabolomics & Proteomics (CF-MPC)
Institute of Experimental Genetics (IEG)
Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
POF Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
30505 - New Technologies for Biomedical Discoveries
30203 - Molecular Targets and Therapies
30201 - Metabolic Health
Forschungsfeld(er) Genetics and Epidemiology
Enabling and Novel Technologies

Pioneer Campus
PSP-Element(e) G-506700-001
G-504000-002
A-630710-001
A-630700-001
G-500600-001
G-504091-004
G-505700-001
G-540004-001
G-510007-001
G-504000-010
Förderungen Qatar National Research Fund (QNRF)
European Union
European Federation of Pharmaceutical Industries and Associations (EFPIA)
German Federal Ministry of Health (Berlin, Germany)
Ministry of Science and Culture in North-Rhine Westphalia (Dusseldorf, Germany)
German Federal Ministry of Education and Research
Helmholtz Zentrum Munchen - German Research Center for Environmental Health - German Federal Ministry of Education and Research (BMBF)
State of Bavaria
Biomedical Research Program at Weill Cornell Medicine in Qatar
Qatar Foundation
Innovative Medicines Initiative 2 Joint Undertaking (JU)
DOI 10.1038/s41467-025-58314-3
WOS ID 001459702500004
Scopus ID 105002967153
PubMed ID 40188173
Erfassungsdatum 2025-04-14
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