DTU Health Tech
Department of Health Technology
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If you need help with the bioinformatics programs, see the "Getting Help" section below the program.
ICERFIRE is an ensemble of Random Forest models. It predicts neo-epitope immunogenicity by identifying the best HLA-binding ICORE and uses self-similarity, a mutation score, and antigen expression.
Accurate prediction of immunogenicity for neo-epitopes is crucial for the development of personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive study of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications.
The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), takes as input the predicted ICORE rather than the full neopeptide as input, i.e. the submer with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank).
Key additional features integrated into the model include self-similarity score, the similarity to the aligned wild-type ICORE to assess self-tolerance; BLOSUM mutation score, scoring a mutant against its wild-type counterpart; and wild-type antigen expression, capturing a neo-epitope’s abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.
If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).
If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.
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