Beyond sequence: A physics-informed machine learning framework for predicting DNA mutations

doi:10.1016/j.csbj.2025.08.033

2025-09-10

Martha Villagrán *

Nikolaos Mitsakos

John H. Miller

DOI：10.1016/j.csbj.2025.08.033

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摘要

En 中文

本研究探讨了整合量子紧束缚模型信息如何能提升机器学习模型对线粒体DNA（mtDNA）突变易感位点的预测能力。我们采用量子哈密顿量技术和机器学习方法，探索线粒体DNA超变区1（HVR1）的突变情况。该区域以其高变异性而著称，并常用于系谱DNA检测与研究。本研究的方法考虑了每个碱基对关联的局部能量，以及DNA链内电子间的相互作用。为此，我们分析了Mitomap数据库的数据。研究结果表明，局部电离能和碱基对的上下文依赖性均显著影响DNA中的突变位点。具体而言，我们的机器学习模型在分析同聚体重复序列（即序列中单个碱基对多次重复的区域）时能够提取有价值的见解。

Keyword:

Machine learning

Mutations

Quantum biology

Homopolymeric runs

Hypervariable region 1

DNA

Mitochondrial DNA

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期刊

Computational and Structural Biotechnology Journal

IF:

4.1

论文数:

713

被引数:

1.4W

机构

university of houston

学者数:

9.7K

论文数: 7.9K

被引数: 11

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