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

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Abstract

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.  

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Abbreviations

MATLAB:

Matrix Laboratory

PERL:

Practical Extraction And Reporting Language

GOLOBOCAN:

Global Cancer Incidence, Mortality, And Prevalence

WHO:

World Health Organization

MHC:

Major Histocompatibility Complex Or Molecules

TCRs:

T Cell Recognition

NGS:

Next Generation Sequence

RNA:

Ribonucleic Acid

DNA:

Deoxyribonucleic Acid

API:

Application Program Interface

ACS:

Anti‐Cancer Scanner

iPCS:

Induced Pluripotent Stem Cells

MARIA:

Major Histocompatibility Complex Analysis With Recurrent Integrated Architecture

CD8 + :

Cluster Of Differentiation 8

CD4 + :

Cluster Of Differentiation 4

SNA:

Spherical Acid Nucleic Acid

XGboost:

Gradient Boosting,

ANOVA:

Analysis Of Variance

ACPS:

Anti‐Cancer Scanner

SASA:

Solvent-Accessible Surface Area Of The Neoepitope

HLA:

Human Leukocyte Antigen

FANTOM:

Functional Annotation Of The Mouse/Mammalian Genome

RNN:

Recurrent Neural Network

USA:

United State Of America

LSTM:

Long Short-Term Memory

MLH1:

Mutl Protein Homolog 1

NLP:

Natural Language Processing

IEDB:

Immunity Epitope Database

AUC:

Area Under Curve

ANN:

Artificial Neural Network

References

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Acknowledgements

We thank the library of Tarbiat Modares University for accessing and analyzing electronic resources.

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  1. Department of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

    Mohaddeseh Nasiri Hooshmand & Elham Maserat

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  1. Mohaddeseh Nasiri Hooshmand
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Mohaddeseh Nasiri Hooshmand: Design, Analysis, Data gathering, Writing.

Elham Maserat: Design, Analysis, Data gathering, Writing.

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Correspondence to Elham Maserat.

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Hooshmand, M.N., Maserat, E. Application of machine learning and deep learning for cancer vaccine (rapid review). Multimed Tools Appl 83, 51211–51226 (2024). https://doi.org/10.1007/s11042-023-17589-8

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