LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer
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...
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58314-3 |
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| author | Siyu Han Shixiang Yu Mengya Shi Makoto Harada Jianhong Ge Jiesheng Lin Cornelia Prehn Agnese Petrera Ying Li Flora Sam Giuseppe Matullo Jerzy Adamski Karsten Suhre Christian Gieger Stefanie M. Hauck Christian Herder Michael Roden Francesco Paolo Casale Na Cai Annette Peters Rui Wang-Sattler |
| author_facet | Siyu Han Shixiang Yu Mengya Shi Makoto Harada Jianhong Ge Jiesheng Lin Cornelia Prehn Agnese Petrera Ying Li Flora Sam Giuseppe Matullo Jerzy Adamski Karsten Suhre Christian Gieger Stefanie M. Hauck Christian Herder Michael Roden Francesco Paolo Casale Na Cai Annette Peters Rui Wang-Sattler |
| author_sort | Siyu Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e4e92326b3bf41d6a466f35ae89242a9 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e4e92326b3bf41d6a466f35ae89242a92025-08-20T02:25:40ZengNature PortfolioNature Communications2041-17232025-04-0116112010.1038/s41467-025-58314-3LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transferSiyu Han0Shixiang Yu1Mengya Shi2Makoto Harada3Jianhong Ge4Jiesheng Lin5Cornelia Prehn6Agnese Petrera7Ying Li8Flora Sam9Giuseppe Matullo10Jerzy Adamski11Karsten Suhre12Christian Gieger13Stefanie M. Hauck14Christian Herder15Michael Roden16Francesco Paolo Casale17Na Cai18Annette Peters19Rui Wang-Sattler20Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental HealthInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental HealthInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental HealthInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental HealthInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental HealthInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental HealthMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental HealthCollege of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin UniversityEli Lilly and Company, Lilly Corporate CenterGenomics Variation, Population Medicine and Complex Diseases Unit, Turin UniversityInstitute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental HealthBioinformatics Core, Weill Cornell Medicine-Qatar, Education CityInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental HealthMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental HealthInstitute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University DüsseldorfInstitute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University DüsseldorfInstitute of AI for Health, Helmholtz Zentrum München, German Research Center for Environmental HealthTUM School of Medicine and Health, Technical University of MunichGerman Center for Diabetes Research (DZD), Partner NeuherbergInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental HealthAbstract 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.https://doi.org/10.1038/s41467-025-58314-3 |
| spellingShingle | Siyu Han Shixiang Yu Mengya Shi Makoto Harada Jianhong Ge Jiesheng Lin Cornelia Prehn Agnese Petrera Ying Li Flora Sam Giuseppe Matullo Jerzy Adamski Karsten Suhre Christian Gieger Stefanie M. Hauck Christian Herder Michael Roden Francesco Paolo Casale Na Cai Annette Peters Rui Wang-Sattler LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer Nature Communications |
| title | LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer |
| title_full | LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer |
| title_fullStr | LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer |
| title_full_unstemmed | LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer |
| title_short | LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer |
| title_sort | leopard missing view completion for multi timepoint omics data via representation disentanglement and temporal knowledge transfer |
| url | https://doi.org/10.1038/s41467-025-58314-3 |
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