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通过早期停止加速最大概率的基因推理来避免 (过度) 优化.
Anastasis Togkousidis1,2,3, Alexandros Stamatakis1,2,4, Olivier Gascuel3
1Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Schloß-Wolfsbrunnenweg 35, Heidelberg 69118, Germany.
Systematic biology
|May 30, 2025
概括
遗传学推断工具现在使用Kishino-Hasegawa (KH) 测试提供了早期停止标准,以防止过度优化. 这种方法显著加快了分析的速度,同时保持了DNA和蛋白质数据的树精度.
科学领域:
- 计算生物学 计算生物学
- 人类遗传学 是一个学科.
- 生物信息学是一种生物信息学.
背景情况:
- 基于最大概率的基因推理是一个易于过度优化和过度拟合的优化问题,因为有噪音的序列数据.
- 现有的方法可能过度优化,导致计算效率低下,进化模型可能不准确.
- 需要可靠的早期停止标准,以平衡优化彻底性与计算成本和数据噪声.
研究的目的:
- 将Kishino-Hasegawa (KH) 测试作为早期停止标准集成到RAxML-NG中,以防止过度优化.
- 开发一个简化的启发式树搜索策略 (sRAxML-NG),作为早期停止方法的基础.
- 建议扩展KH测试,用于多次测试的校正,以提高速度和准确性.
主要方法:
- 在RAxML-NG中实施了一个简化的启发式树搜索策略 (sRAxML-NG).
- 整合了Kishino-Hasegawa (KH) 测试,以统计评估中间遗传树之间的改进.
- 开发并应用KH测试的多重测试校正扩展以提高性能.
- 从TreeBASE.使用300个经验DNA和氨基酸 (AA) 数据集进行基准性能测试.
主要成果:
- 使用KH测试和sRAxML-NG的早期停止方法,在98%的DNA数据集中实现了与RAxML-NG v1.2统计学上相当的树.
- 对于AA数据集,sRAxML-NG,KH和KH多重测试版本分别在96%,95%和92%的案例中产生了统计学上相当的树.
- 与RAxML-NG v1.2.2.相比,KH多重测试版本与sRAxML-NG提供了DNA的平均加速度5倍,蛋白质数据集的平均加速度3.9倍.
结论:
- 实施的早期停止标准,特别是多次测试校正的KH测试,有效地防止了基因推断中的过度优化.
- 这些方法提供了显著的计算加速,而不会影响推断的家族遗传树的统计准确性.
- 早期停止标准现在已经集成到RAxML-NG中,为家族遗传学分析提供了更有效的工具.
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