neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival

neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival

Background: Neoantigen based personalized immune therapies achieve promising results in melanoma and lung cancer, but few neoantigen based models perform well in IDH wild-type GBM, and the association between neoantigen intrinsic features and prognosis remain unclear in IDH wild-type GBM. We present...

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Bibliographic Details
Main Authors: Chen, Y. (Author), Fan, Y. (Author), Han, X. (Author), He, Y. (Author), Li, J. (Author), Li, L. (Author), Li, W. (Author), Liu, G. (Author), Liu, Y. (Author), Sun, T. (Author), Wang, H. (Author), Wang, L. (Author), Wen, H. (Author), Xiao, Z. (Author), Zhang, J. (Author), Zhang, W. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Brain Neoplasms
brain tumor
cohort analysis
Cohort Studies
Deep learning
Deep Learning
genetics
glioblastoma
Glioblastoma
Glioblastomas
High potential
human
Humans
IDH wild-type glioblastoma
Immunogenicity
Immunology
Intrinsic features
isocitrate dehydrogenase
Isocitrate Dehydrogenase
Learning models
Learning systems
Leave-one-out cross-validation (LOOCV)
Lung Cancer
Most likely
Peptide-features
Population statistics
Prognosis
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