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Updated: Sep 9, 2025
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使用神经网络和集体学习从基因树进行参数估计
Tianjian Qin1, Koen J van Benthem1, Luis Valente1,2
1Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7, Groningen, 9747 AG, The Netherlands.
Systematic biology
|September 3, 2025
概括
这项研究引入了一个集合神经网络,以估计从基因树的物种多样化率,为许多场景提供了更快,更少偏差的最大概率估计 (MLE) 替代方案.
科学领域:
- 进化生物学
- 计算生物学
- 遗传学
背景情况:
- 物种多样化是由物种化和灭绝驱动的.
- 从字型来估计这些比例至关重要,但具有挑战性.
- 目前的最大概率估计 (MLE) 方法在复杂的模型和小族系方面存在局限性.
研究的目的:
- 开发和评估一种新的集合神经网络方法,用于估计从家族遗传树的多样化参数.
- 将神经网络方法的性能与MLE和现有的深度学习方法进行比较.
- 评估神经网络在处理基因数据方面的稳定性.
主要方法:
- 一个集体神经网络结合了密集的神经网络,图形神经网络和循环神经网络.
- 网络从图表表示,分支时间和族群统计总结中学习.
- 使用模拟的遗传学数据对MLE和卷积网络方法进行了性能评估.
主要成果:
- 集合神经网络提供比MLE更快的估计,并且对恒定速率和多样性依赖模型的树大小敏感度较低.
- 它的性能与现有的卷积网络方法相提并论.
- 在延长的出生死亡过程中,神经网络方法和MLE都难以恢复参数.
结论:
- 整体神经网络是MLE的一个有希望的,更快,更不偏的替代方案,用于估计多元化参数,特别是当MLE不可行时.
- 生殖系大小和进化信号的强度是准确参数估计的关键限制.
- 当足够的基因信号存在时,该方法表现良好.
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