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Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has...

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Autores principales: Cai, Yu, Chen, Rui, Gao, Shenghan, Li, Wenqing, Liu, Yuru, Su, Guodong, Song, Mingming, Jiang, Mengju, Jiang, Chao, Zhang, Xi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868469/
https://www.ncbi.nlm.nih.gov/pubmed/36698417
http://dx.doi.org/10.3389/fonc.2022.1054231
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author Cai, Yu
Chen, Rui
Gao, Shenghan
Li, Wenqing
Liu, Yuru
Su, Guodong
Song, Mingming
Jiang, Mengju
Jiang, Chao
Zhang, Xi
author_facet Cai, Yu
Chen, Rui
Gao, Shenghan
Li, Wenqing
Liu, Yuru
Su, Guodong
Song, Mingming
Jiang, Mengju
Jiang, Chao
Zhang, Xi
author_sort Cai, Yu
collection PubMed
description The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.
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spelling pubmed-98684692023-01-24 Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy Cai, Yu Chen, Rui Gao, Shenghan Li, Wenqing Liu, Yuru Su, Guodong Song, Mingming Jiang, Mengju Jiang, Chao Zhang, Xi Front Oncol Oncology The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed. Frontiers Media S.A. 2023-01-09 /pmc/articles/PMC9868469/ /pubmed/36698417 http://dx.doi.org/10.3389/fonc.2022.1054231 Text en Copyright © 2023 Cai, Chen, Gao, Li, Liu, Su, Song, Jiang, Jiang and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Cai, Yu
Chen, Rui
Gao, Shenghan
Li, Wenqing
Liu, Yuru
Su, Guodong
Song, Mingming
Jiang, Mengju
Jiang, Chao
Zhang, Xi
Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
title Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
title_full Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
title_fullStr Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
title_full_unstemmed Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
title_short Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
title_sort artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868469/
https://www.ncbi.nlm.nih.gov/pubmed/36698417
http://dx.doi.org/10.3389/fonc.2022.1054231
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