SIGANEO: Similarity network with GAN enhancement for immunogenic neoepitope prediction.

ABSTRACT: Target selection of the personalized cancer neoantigen vaccine, which is highly dependent on computational prediction algorithms, is crucial for its clinical efficacy. Due to the limited number of experimentally validated immunogenic neoepitopes as well as the complexity of neoantigens in eliciting T cell response, the accuracy of neoepitope immunogenicity prediction methods requires persistent efforts for improvement. We present a deep learning framework for neoepitope immunogenicity prediction - SIGANEO by integrating GAN-like network with similarity network to address issues of missing values and limited data concerning neoantigen prediction. This framework exhibits superior performance over competing machine-learning-based neoantigen prediction algorithms over an independent test datas

SUBMITTER: Ye Y 

PROVIDER: S-EPMC10681954 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature