一种基于机器学习的方法来预测欧洲内的生物地理祖先
Anna Kloska1,2, Agata Giełczyk3, Tomasz Grzybowski1
1Department of Forensic Medicine, The Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85067 Bydgoszcz, Poland.
International journal of molecular sciences
|October 28, 2023
概括
大规模并行测序 (MPS) 和机器学习 (ML) 可以区分斯拉夫人的个体. 支持矢量机 (SVM) 在分类来自不同斯拉夫族群的DNA样本时实现了99.9%的准确性.
科学领域:
- 人口遗传学 人口遗传学
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 大规模并行测序 (MPS) 产生了大量数据集,对人口遗传学有价值.
- 机器学习 (ML) 技术非常适合分析复杂的MPS数据.
- 斯拉夫人人口占欧洲人口的很大一部分,具有密切的遗传联系.
研究的目的:
- 评估使用ML来分类斯拉夫人和非斯拉夫人的DNA样本的可行性.
- 评估在密切相关的斯拉夫人群体中辨别遗传来源的能力.
- 为了分类来自不同斯拉夫人口代表的DNA标本.
主要方法:
- 将原始测序数据预处理成一个 1200 个字符的二进制向量.
- 三种ML分类器的应用:随机森林,支持矢量机 (SVM) 和XGBoost.
- 对遗传祖先推断分类器性能的实证评估.
主要成果:
- 具有线性内核的支持矢量机 (SVM) 展示了卓越的性能.
- 在分类斯拉夫人和非斯拉夫人的个人中达到99.9%的准确性.
- 在所有类别中获得高F1分数 (0.9846-1.000),表明强大的分类.
结论:
- ML,特别是SVM,在大型测序数据集中对遗传祖先的分类非常有效.
- 这种方法可以准确地区分不同斯拉夫人群的个体,即使是那些具有密切遗传起源的个体.
- 这项研究证实了MPS和ML在详细的人口遗传学研究中的潜力.
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