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<b>Background</b> <p>Molecular subtyping is essential for precision oncology, enabling the classification of tumors into biologically and clinically relevant categories. DNA methylation has emerged as a promising biomarker for cancer subtyping, yet its application remains limited by high dimensionality, batch effects, and the lack of automated, user-friendly analytical tools.</p> Results <p>Here, we present CancerSubtyper, an end-to-end computational framework for deep learning–based cancer subtyping using DNA methylation data, which is accessible through an intuitive web interface designed to support interactive exploration and downstream analysis. CancerSubtyper integrates two complementary models: a semi-supervised classifier for cancers with well-established subtypes, and a hybrid framework that integrates supervised and unsupervised learning to identify novel subtypes. The framework automatically performs preprocessing, feature selection, batch correction, and cancer subtyping while offering interactive visualization for subtype exploration and validation.</p> Conclusion <p>By providing an automated, end-to-end workflow accessible through a user-friendly web interface, CancerSubtyper lowers the barrier to large-scale methylation analysis and provides a powerful tool for molecular subtyping and precision oncology research. The framework is freely accessible at <a href="https://github.com/ycheung5/cancersubtyper/">https://github.com/ycheung5/cancersubtyper/</a>.</p>

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CancerSubtyper: a deep learning framework for cancer subtyping through DNA methylation data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月27日

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  • Joung Min Choi,
  • Yat Fei Cheung,
  • Liqing Zhang

摘要

Background

Molecular subtyping is essential for precision oncology, enabling the classification of tumors into biologically and clinically relevant categories. DNA methylation has emerged as a promising biomarker for cancer subtyping, yet its application remains limited by high dimensionality, batch effects, and the lack of automated, user-friendly analytical tools.

Results

Here, we present CancerSubtyper, an end-to-end computational framework for deep learning–based cancer subtyping using DNA methylation data, which is accessible through an intuitive web interface designed to support interactive exploration and downstream analysis. CancerSubtyper integrates two complementary models: a semi-supervised classifier for cancers with well-established subtypes, and a hybrid framework that integrates supervised and unsupervised learning to identify novel subtypes. The framework automatically performs preprocessing, feature selection, batch correction, and cancer subtyping while offering interactive visualization for subtype exploration and validation.

Conclusion

By providing an automated, end-to-end workflow accessible through a user-friendly web interface, CancerSubtyper lowers the barrier to large-scale methylation analysis and provides a powerful tool for molecular subtyping and precision oncology research. The framework is freely accessible at https://github.com/ycheung5/cancersubtyper/.