Application of machine learning and deep learning for cancer vaccine (rapid review)

Cancer is a common and dangerous disease based on the World Health Organization. Much research has been done on new and effective cancer treatments, including cancer vaccines and the prediction of neoantigens using machine learning. The purpose of this study is to review articles that use machine learning to design cancer vaccines. This study is a rapid review study using search strategies and related keywords in Google Scholar, PubMed, and science direct databases from 2010 to 2021 in 2021 and revised in August 2023. 1250 articles were searched and 13 articles were selected for this review. We investigated them and then due to the importance and popularity of using machine learning in cancer vaccines recently, we compared them based on their machine learning technique. it is shown that neural networks with Python are used to predict neoantigens in 4 articles and with MATLAB in 2 articles, one article was about using the Fontom, one article with PERL, and one article with R; Other studies were about data mining with flowsom algorithm, multiple linear regression, logistics, and oncopepVCA, and the rest of articles do not provide information about machine learning implementation tools. Providing neural networks with Python is useful in the prediction of neoantigens due to the precision and examination of complex data sets. They use to predict HLA and peptide binding affinity, vaccines outcome, personalized cancer vaccines based on new data, the immune response, processing RNA and DNA sequences, and immunological analysis.