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Joint representation learning for oncology applications.

Tanya Nandan1,2, Bowen Fan3,4, Samuel Håkansson2

  • 1Department of Biosystems Sciences and Engineering (D BSSE), ETH Zürich, Zürich 8092, Switzerland.

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|October 29, 2025
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Summary
This summary is machine-generated.

This study introduces Joint Multidimensional Scaling (Joint MDS), a novel method for integrating multi-modal cancer data, improving understanding of tumor biology. Joint MDS enhances data integration accuracy and computational approaches for complex diseases.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Data science

Background:

  • Integrating multi-modal data (imaging, molecular) advances cancer biology understanding.
  • Heterogeneous, high-dimensional data integration presents computational challenges.

Purpose of the Study:

  • To introduce Joint Multidimensional Scaling (Joint MDS) for unsupervised multi-modal data integration.
  • To extend Joint MDS to a three-modality framework (Joint MDS3).

Main Methods:

  • Applied Joint MDS to integrate radiomic (MRI) with transcriptomic, epigenomic, and CNV data from glioma patients.
  • Extended the method to Joint MDS3 for three-modality integration.

Main Results:

  • Joint MDS outperformed baseline Pamona and achieved competitive performance against SCOTv2.
  • Achieved an average label transfer accuracy of 74.8%, a 4% improvement over baselines.
  • Reduced the fraction of samples closer to an incorrect match (FOSCTTM) to 51% or less.

Conclusions:

  • Joint MDS effectively integrates diverse data types into a unified representation.
  • This approach advances computational methods for analyzing complex diseases like cancer.