通过监督机器学习从等位基频率推断计算高效的人口历史推断
Linh N Tran1,2, Connie K Sun2, Travis J Struck2
1Genetics Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA.
bioRxiv : the preprint server for biology
|February 26, 2024
概括
我们开发了donni,一种新的神经网络方法,可以从基因组数据中推断人口人口统计历史. 唐尼提供了更快,更高效的计算效率的人口推断,其准确性与现有方法相美.
科学领域:
- 人口遗传学 人口遗传学
- 基因组数据分析.
- 计算生物学是一种计算生物学.
背景情况:
- 从基因组数据推断人口人口统计历史在科学领域至关重要.
- 基于等位基频谱 (AFS) 和最大复合概率优化的达迪方法被广泛使用,但计算密集.
- 需要更有效的人口推理方法.
研究的目的:
- 开发一个计算高效的人口历史推断方法.
- 保持可比的推断准确度,以现有的方法,如 dadi.
- 为了利用监督机器学习进行人口推断.
主要方法:
- 开发了donni (通过神经网络推断优化人口统计),一种基于dadi的方法.
- 模拟各种人口模型和参数的预期AFS.
- 训练有素平均差异估计神经网络使用模拟的AFS进行快速参数推断.
主要成果:
- 唐尼很好地推断出人口规模的变化,而迁移率/事件时间也相当好.
- 提供参数和置信区间估计,其准确度与 dadi.com 相比.
- 显著绕过了大迪的概率优化计算密集的评估过程.
结论:
- 唐尼为人口历史推断提供了一个更有效的替代方案.
- 监督机器学习为开发可持续和计算效率高的人口推断工具提供了一个有希望的方法.
- 这一进步促进了基因组数据在理解人口动态方面的更广泛应用.
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