biostudies-literatureZhao XChinese Government ScholarshipNational Natural Science Foundation of ChinaTsinghua-Toyota Joint Research Institute Inter-Disciplinary Program13https://www.ebi.ac.uk/biostudies/studies/S-EPMC12838086biostudies-literatureUnknown18(1)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/XuegGenome medicineNeoGuider: neoepitope prediction using advanced feature engineering.PMC1283808662373210202439300932022GXZ00580692470105Xie ZZhang XWei LZhao XfalseNeoGuider: neoepitope prediction using advanced feature engineering.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/Xueg2025-01-01T00:00:00Z2025 Dec2026-06-11T05:34:33.756Z2026-06-11T03:08:24.772ZS-EPMC128380864143728010.1186/s13073-025-01592-9