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Updated: Oct 29, 2025

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Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance

Marleen M Nieboer1,2, Luan Nguyen1,2, Jeroen de Ridder3,4

  • 1Center for Molecular Medicine, University Medical Center Utrecht, 3584 CG, Utrecht, The Netherlands.

Scientific Reports
|July 14, 2021
PubMed
Summary

This study introduces svMIL2, a machine learning tool to identify pathogenic non-coding structural variants (SVs) in cancer genomes. It highlights the significant role of these non-coding SVs in disrupting gene regulation and driving cancer development.

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