{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhao X"],"funding":["Chinese Government Scholarship","National Natural Science Foundation of China","Tsinghua-Toyota Joint Research Institute Inter-Disciplinary Program"],"pagination":["13"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12838086"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(1)"],"pubmed_abstract":["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/Xueg"],"journal":["Genome medicine"],"pubmed_title":["NeoGuider: neoepitope prediction using advanced feature engineering."],"pmcid":["PMC12838086"],"funding_grant_id":["62373210","20243930093","2022GXZ005806","92470105"],"pubmed_authors":["Xie Z","Zhang X","Wei L","Zhao X"],"additional_accession":[]},"is_claimable":false,"name":"NeoGuider: neoepitope prediction using advanced feature engineering.","description":"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/Xueg","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-11T05:34:33.756Z","creation":"2026-06-11T03:08:24.772Z"},"accession":"S-EPMC12838086","cross_references":{"pubmed":["41437280"],"doi":["10.1186/s13073-025-01592-9"]}}