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Hongru Shen¹, Jilei Liu¹, Kexin Chen²

¹Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.

Briefings in bioinformatics

|February 22, 2024

概括

我们开发了负担得起的癌症拦截和诊断 (ACID) 模型,用于使用无细胞DNA (cfDNA) 测序读取的癌症诊断. ACID在检测癌症和肝细胞癌症方面表现出卓越的性能,提供了负担得起和高效的诊断方法.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

早期癌症检测对于改善患者的治疗结果至关重要.
目前使用无细胞DNA (cfDNA) 诊断癌症的方法可能是复杂且昂贵的.
分析原始测序读数直接对诊断模型构成挑战.

研究的目的:

为癌症诊断引入负担得起的癌症拦截和诊断 (ACID) 模型.
通过使用原始cfDNA测序来评估ACID的性能,阅读.
为癌症检测建立一个高效,负担得起的端到端模式.

主要方法:

制定了ACID作为一种自回归语言模型.
预训练ACID使用连接的原始测序读取和诊断标签.
与全基因组和二硫酸盐测序数据集的三种现有方法进行基准ACID.

主要成果:

在整体癌症诊断 (AUROC,0.924对0.853) 和肝细胞癌检测 (AUROC,0.981对0.917) 中,ACID显著优于最佳基准方法.
通过每样本仅使用1万次读数,ACID实现了高精度.
与基准方法相比,ACID在双硫酸盐测序数据集上表现优异.

结论:

ACID为癌症检测提供了一种负担得起,简单和高效的端到端方法.
该模型有效地利用原始cfDNA测序读取用于诊断.
在癌症诊断方面,ACID显示出改善癌症诊断的可访问性和准确性的前景.

关键词:

癌症检测癌症检测没有细胞的DNA DNA.生成性语言模型的模型.

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