Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms

BackgroundDeveloping prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of i...

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Bibliographic Details
Main Authors: Zhichao Liu, Guo Lin, Zeping Yan, Linduo Li, Xingchen Wu, Jingrong Shi, Jianxing He, Lei Zhao, Hengrui Liang, Wei Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Immunology
Subjects:
non-small cell lung cancer (NSCLC)
machine learning (ML)
immunotherapy
gene
tumor mutational burden (TMB)
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2022.989275/full

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https://www.frontiersin.org/articles/10.3389/fimmu.2022.989275/full

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