TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits

TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its...

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Bibliographic Details
Main Authors: Yixuan Wang, Jiayin Wang, Wenfeng Fang, Xiao Xiao, Quan Wang, Jian Zhao, Jingjing Liu, Shuanying Yang, Yuqian Liu, Xin Lai, Xiaofeng Song
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-05-01
Series:Frontiers in Immunology
Subjects:
clinical immunology
multidimensional tumor mutation burden
multiple instance learning
categorical decision-making
statistical interpretability
model calibration
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2023.1151755/full

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https://www.frontiersin.org/articles/10.3389/fimmu.2023.1151755/full

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