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  3. Vfling: Vertical Federated Learning For Multi-omics Data Integration With Graphs.
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  3. Vfling: Vertical Federated Learning For Multi-omics Data Integration With Graphs.

Related Experiment Video

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

VFLING: Vertical Federated Learning for Multi-Omics Data Integration with Graphs.

Xiaoli Li1,2,3, Qi Li4,5, Dedao Lu2,6

  • 1Centre for Cognitive and Brain Sciences, University of Macau, Taipa, China.

Interdisciplinary Sciences, Computational Life Sciences
|May 8, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Federated learning for multi-omics data (VFLING) enhances privacy and accuracy by sharing local features and graph topology in one communication. This approach improves model performance, even with missing data, for medical applications.

Keywords:
Graph topologyMulti-omics integrationMutual informationOne-shot communicationVertical federated learning

Related Experiment Videos

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • Federated learning for multi-omics data faces privacy and performance challenges.
  • Communication overhead and missing features degrade traditional federated learning models.

Purpose of the Study:

  • To propose VFLING (Federated Learning for Multi-Omics Data Integration with Graphs), a secure federated learning framework.
  • To address privacy concerns and improve federated learning performance with multi-omics data.

Main Methods:

  • Developed a secure one-shot communication federated learning framework (VFLING).
  • Transmitted local features and graph topology to a central server in a single communication.
  • Fused omics data based on locally learned graph structures, not just features.

Main Results:

  • VFLING demonstrated superior performance compared to existing federated learning frameworks.
  • The framework showed improved robustness and accuracy, even with incomplete feature sets across parties.
  • One-shot communication of features and graph topology maximized information transfer.

Conclusions:

  • VFLING offers a privacy-preserving and efficient solution for multi-omics data integration using federated learning.
  • The graph-based fusion approach enhances model accuracy and robustness in medical applications.
  • This framework facilitates secure collaborative analysis of sensitive patient data.