评估基于模拟的监督机器学习以从基因组数据推断人口参数推断
Arnaud Quelin1,2, Frédéric Austerlitz3, Flora Jay4
1UMR 7206 Eco-Anthropologie (EA), CNRS, Muséum National d'Histoire Naturelle, Université Paris Cité, Paris, France. arnaud.quelin@mnhn.fr.
Heredity
|June 5, 2025
概括
机器学习方法,特别是多层感知器 (MLP),有效地从基因组数据中推断出人口人口统计历史. 这些先进的技术通过使用全面的总结统计数据,优于传统的近似贝叶斯计算 (ABC).
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.
- 进化基因组学是进化的基因组学.
背景情况:
- 高通量DNA测序产生了大量的基因组数据,推进了人口进化和人口历史研究.
- 人口推断方法,包括基于模拟的近似贝叶斯计算 (ABC),利用基因组数据,但通常使用部分信息.
- 机器学习 (ML) 提供了整合更全面的基因组信息的潜力,以改善人口统计推断.
研究的目的:
- 评估基于模拟的监督机器学习方法,以推断连接群体中的人口参数.
- 将多层感知子 (MLP),随机森林 (RF) 和XGBoost (XGB) 与传统的ABC算法的性能进行比较.
- 通过使用可解释的人工智能 (XAI) 调查各种总结统计数据对人口推断的贡献.
主要方法:
- 在基因组数据上应用了三个监督机器学习方法 (MLP,RF,XGBoost).
- 使用了广泛的汇总统计数据,与迁移和二次接触模型隔离.
- 使用SHAP (夏普利添加式解释) 进行模型解释性和特征重要性分析.
主要成果:
- 与随机森林 (RF) 和XGBoost (XGB) 相比,多层感知子 (MLP) 显示出更高的性能.
- MLP预测包含了更广泛的总结统计数据组合,根据换特征的重要性来表示.
- 所有测试的机器学习方法在人口推断中都超过了三个近似贝叶斯计算 (ABC) 算法.
结论:
- 基于模拟的监督机器学习,特别是MLP,对于从基因组数据中推断复杂的人口人口历史非常有效.
- 随着MLP能够整合多样化的总结统计数据的能力,它在人口统计推断中提高了准确性.
- 像SHAP这样的可解释的人工智能方法为人口遗传学机器学习模型中的特征贡献提供了宝贵的见解.
相关概念视频
01:12Analysis of Population Pharmacokinetic Data
397
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
397
01:26Distributions to Estimate Population Parameter
4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.3K
01:25What is Population Genetics?
59.8K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
59.8K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
89
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
89
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
104
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
104
01:25Statistical Methods for Analyzing Epidemiological Data
553
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
553