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Updated: May 17, 2025

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

Siyu Han1,2,3, Shixiang Yu1,2,3, Mengya Shi1,2,3

  • 1Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

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|April 5, 2025
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Summary
This summary is machine-generated.

LEOPARD effectively completes missing views in longitudinal multi-omics data by separating content and temporal information. This novel approach enhances personalized healthcare insights from complex biological datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Longitudinal multi-view omics data provide insights into physiological changes over time.
  • Incomplete omics datasets hinder the analysis of temporal dynamics and personalized medicine.

Purpose of the Study:

  • To introduce LEOPARD, a novel method for completing missing views in longitudinal multi-omics data.
  • To address the challenge of extrapolation tasks with incomplete temporal omics datasets.

Main Methods:

  • LEOPARD disentangles longitudinal omics data into content and temporal representations.
  • Temporal knowledge is transferred to omics-specific content to impute missing views.
  • Validated on four real-world omics datasets from MGH COVID study and KORA cohort.

Main Results:

  • LEOPARD demonstrated superior performance compared to conventional imputation methods (missForest, PMM, GLMM, cGAN).
  • Imputed data showed high agreement with observed data for metabolite and protein identification.
  • Achieved accurate chronic kidney disease prediction using LEOPARD-imputed data.

Conclusions:

  • LEOPARD offers a generalized approach for handling missing views in longitudinal omics data.
  • Enables comprehensive exploration of temporal dynamics for personalized healthcare advancements.