计算效率高的人口历史推断从同位素频率与监督机器学习的推断
Linh N Tran1,2, Connie K Sun2, Travis J Struck2
1Genetics Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ 85721, USA.
Molecular biology and evolution
|April 18, 2024
概括
我们开发了donni,这是一个更快的方法,可以从基因组数据中推断人口人口统计历史. 它使用神经网络来实现与旧方法相似的准确性,但显著减少了计算时间.
科学领域:
- 人口遗传学 人口遗传学
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 从基因组数据推断过去的人口历史在进化生物学中至关重要.
- 代基频谱 (AFS) 是人口统计推断的一个关键总结统计数据.
- 像达迪这样的现有方法可能是计算密集的.
研究的目的:
- 开发一种更高效的计算方法来推断人口统计历史.
- 保持与现有的基于概率的方法可比的准确性.
- 为了利用机器学习进行人口推断.
主要方法:
- 唐尼 (通过神经网络推断进行人口统计优化) 使用中位方差估计神经网络.
- 该方法模拟了各种人口模型和参数的AFS.
- 训练有素的神经网络立即从AFS中推断出模型参数.
主要成果:
- 对于参数和置信区间估计,Donni的准确性与达迪相似.
- 该方法显著降低了人口推理的计算成本.
- 唐尼准确地推断出人口规模的变化,以及其他参数,如迁移率相当好.
结论:
- 唐尼为人口历史推断提供了一个计算效率高的替代方案.
- 监督机器学习为开发可持续推理方法提供了一个有希望的方法.
- 这项工作有助于对人口历史进行大规模的基因组研究.
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