Artificial intelligence and immunotherapy

Figures & data

Figure 1. Machine learning methodologies frequently used in immunology: In order for data to be processed by machine learning algorithms, they should be first preprocessed. There are different preprocessing methods such as denoising, feature extraction, etc., based on the type of data used. Then these data are processed using different algorithms in machine learning.

Table 1. Some of the public databases used to develop algorithms in the field of immunology using machine learning methodologies.

Database	Database description	Study/method
TCGA	A database of genetic alterations leading to cancer.	[2], LUADpp
UniProt	A database for functional and structural information and sequence of proteins.	[14], ELM-MHC
IEDB	A database for antibody and T cell epitopes.	[15], NetMHCpan
SYFPEITHI	A database for peptide sequences that bind to MHC I and MHC II.	[15], NetMHCpan

IEDB: Immune Epitope Database, TCGA: The Cancer Genome Atlas

Yu J, Hu Y, Xu Y, et al. LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features. BMC Cancer. 2019 March 22;19(1):263.

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Li Y, Niu M, Zou Q. ELM-MHC: an Improved MHC identification method with extreme learning machine algorithm. J Proteome Res. 2019 Mar 1;18(3):1392–1401.

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Hoof I, Peters B, Sidney J, et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics. 2009;61(1):1–13.

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