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解决癌症基因组差异的深度学习方法:系统性审查和临床视角

Muhammad Zubair¹, Ali Haider Khan¹, Syed Fakhar Bilal¹

¹Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Briefings in bioinformatics

|November 2, 2025

概括

深度学习 (DL) 模型通过减少30-40%的虚假阴性,显著改善了癌症突变检测. 本综述综合了基因组差异的DL方法,有助于精确的癌症治疗.

科学领域:

生物信息学是一种生物信息学.
计算瘤学计算瘤学
基因组数据分析基因组数据分析

背景情况:

癌症测序数据含有差异,挑战精确的突变检测,导致误诊.
深度学习 (DL) 提供了一种有希望的方法来识别和纠正基因组数据中的这些错误.
缺乏对DL架构,性能和癌症基因组学临床翻译的全面评估.

研究的目的:

系统地审查DL方法的最新进展,以识别癌症测序数据中的基因组差异.
评估不同DL架构的性能,并确定最先进的方法.
为DL在精确癌症治疗中的临床转化提供见解.

主要方法:

2015年至2024年间发表的78项研究的系统文献综述.
对癌症基因组数据分析应用的DL方法的综合,重点是变异调用和瘤分层.
分析DL模型的性能,包括减少假阴性率和确定病原变异优先级的准确性.

主要成果:

卷积和基于图形的DL架构在变体调用和瘤分层方面展示了最先进的性能.
与传统管道相比,DL模型将虚假负率降低30%-40%.
像MAGPIE这样的特定方法在优先考虑致病变体方面达到92%的准确性,尽管数据稀缺性和可解释性等挑战仍然存在.

结论:

DL显著提高了癌症突变检测的准确性并减少了错误,为改进诊断和治疗策略提供了途径.
提出了联合学习和注意力机制,以应对数据隐私和模型透明度挑战.
通过DL将生物信息学和瘤学联系起来,可以加快其在精确癌症治疗中的部署.

关键词:

癌症基因组学癌症基因组学临床生物信息学临床生物信息学深度学习是一种深度学习.基因组差异的基因组差异多主题整合多主题整合.突变检测检测突变检测变量调用变量调用整体外基因组测序 (WES) 测序

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