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<p>The design of neoepitope-based cancer immunotherapy requires predicting the immunogenicity of each patient-specific neoepitope candidate. We designed NeoGuider, a machine-learning model to address nonlinearity and class imbalance in such prediction. Central to NeoGuider is a supervised feature-transformation approach, which estimates odds using our custom kernel density estimation followed by centered isotonic regression. We implemented NeoGuider as a bioinformatics pipeline, which detects neoepitope candidates from sequencing data and prioritizes such candidates by predicted immunogenicity. Benchmarking on 7 cohorts, 113 patients, and 635 immunogenic candidates showed that NeoGuider outperformed existing methods in neoepitope prediction. NeoGuider is open-source at <a href="https://github.com/XuegongLab/neoguider">https://github.com/XuegongLab/neoguider</a>.</p>

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NeoGuider: neoepitope prediction using advanced feature engineering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月23日

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  • Xiaofei Zhao,
  • Lei Wei,
  • Zhen Xie,
  • Xuegong Zhang

摘要

The design of neoepitope-based cancer immunotherapy requires predicting the immunogenicity of each patient-specific neoepitope candidate. We designed NeoGuider, a machine-learning model to address nonlinearity and class imbalance in such prediction. Central to NeoGuider is a supervised feature-transformation approach, which estimates odds using our custom kernel density estimation followed by centered isotonic regression. We implemented NeoGuider as a bioinformatics pipeline, which detects neoepitope candidates from sequencing data and prioritizes such candidates by predicted immunogenicity. Benchmarking on 7 cohorts, 113 patients, and 635 immunogenic candidates showed that NeoGuider outperformed existing methods in neoepitope prediction. NeoGuider is open-source at https://github.com/XuegongLab/neoguider.