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通过半监督的GAN模型预测细胞自由DNA的体突变起源

Fahimeh Palizban¹, Mohammadmahdi Sarbishegi², Kaveh Kavousi¹

¹Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.

|November 4, 2024

概括

一个新的机器学习模型准确地区分了癌症突变与细胞自由DNA (cfDNA) 中的克隆血液形成变异. 这一进步提高了液体活检的准确性,以更好地诊断和治疗癌症.

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:

在无细胞DNA (cfDNA) 中区分致病性癌症突变与克隆性血液形成 (CH) 变异对于准确的液体活检诊断至关重要.
错误分类可能导致错误的诊断和低于最佳的治疗策略.

研究的目的:

开发一种机器学习技术,以区分cfDNA中的瘤衍生突变和CH相关突变.
提高液体活检分析的准确性和可靠性.

主要方法:

开发了一个基于半监督生成对抗网络 (SSGAN) 架构的深度学习模型.
创建了大约25,000个单核酸变体 (SNV) 的内部参考目录,具有已知的瘤或CH起源.
使用基因组坐标和cfDNA变体的核酸组成训练模型.

主要成果:

通过SSGAN模型,在对未知的cfDNA变异进行分类时,曲线下面积 (AUC) 达到了95%.
证明了该模型能够准确区分瘤和CH突变之间的能力.
验证了用于液体活检的基因组特征预测的潜力.

结论:

使用cfDNA数据进行基因组特征预测,为传统的多分析体测序提供了强大的替代方案.
先进的数据分析和机器学习为改进基因组学和个性化医学提供了巨大的潜力.
开发的方法提高了在液体活检数据中区分CH和瘤突变的准确性.

关键词:

癌症癌症癌症癌症没有细胞的DNA (cfDNA)克隆性血液形成 (CH)基因组学就是基因组学.一个半监督的生成对抗网络.半监督学习半监督学习身体突变的体质突变

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