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Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy
被引:30
作者:

Cai, Yu
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机构:
Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Chen, Rui
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机构:
Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Gao, Shenghan
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Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Li, Wenqing
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Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Liu, Yuru
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Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Su, Guodong
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Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Song, Mingming
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机构:
Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Jiang, Mengju
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机构:
Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Jiang, Chao
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机构:
Xian Med Univ, Affiliated Hosp 2, Dept Neurol, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China

Zhang, Xi
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h-index: 0
机构:
Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China
机构:
[1] Northwest Univ, Sch Med, Xian, Shaanxi, Peoples R China
[2] Xian Med Univ, Affiliated Hosp 2, Dept Neurol, Xian, Shaanxi, Peoples R China
基金:
中国国家自然科学基金;
关键词:
neoantigen prediction;
cancer neoantigen;
cancer immunotherapy;
artificial intelligence;
next generation sequencing;
MHC CLASS-I;
REACTIVE T-CELLS;
PEPTIDE BINDING;
PREDICTION;
TUMOR;
IMMUNOGENICITY;
POTENTIALS;
PIPELINE;
D O I:
10.3389/fonc.2022.1054231
中图分类号:
R73 [肿瘤学];
学科分类号:
100214 ;
摘要:
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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Tech Univ Denmark, Dept Bio & Hlth Informat, Kemitorvet 208, DK-2800 Lyngby, Denmark
Harvard Med Sch, Boston Childrens Hosp, Computat Hlth Informat Program, Boston, MA USA Tech Univ Denmark, Dept Bio & Hlth Informat, Kemitorvet 208, DK-2800 Lyngby, Denmark

Eklund, Aron Charles
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机构:
Tech Univ Denmark, Dept Bio & Hlth Informat, Kemitorvet 208, DK-2800 Lyngby, Denmark Tech Univ Denmark, Dept Bio & Hlth Informat, Kemitorvet 208, DK-2800 Lyngby, Denmark