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适应性RAxML-NG:在使用数据集难度的最大概率下加速基因推理
Anastasis Togkousidis1, Oleksiy M Kozlov1, Julia Haag1
1Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.
Molecular biology and evolution
|October 7, 2023
概括
这项研究引入了一个适应式树搜索启发式的基因推理推理,通过根据数据集难度调整搜索彻底性来优化计算效率. 这种新方法显著加快了家族遗传树的搜索速度,特别是对于简单和困难的数据集,同时保持了高准确度.
科学领域:
- 计算生物学 计算生物学
- 人类遗传学 是一个学科.
- 机器学习在生物信息学中的应用
背景情况:
- 使用最大概率的家族遗传推断依赖于启发式树搜索,这可能是计算密集的.
- 数据集的难度,与最佳树拓的数量和分辨率有关,影响了搜索效率和趋同.
- 机器学习方法可以预测遗传学数据集的难度,为优化推理策略提供了潜力.
研究的目的:
- 开发和实施RAxML-NG中的自适应树搜索启发式,根据预测的族系遗传数据集难度调整搜索彻底性.
- 通过将搜索策略定制为数据集特征来提高家族遗传树推断的计算效率.
- 在大量实证和模拟数据集中评估适应式启发式的性能和准确性.
主要方法:
- 在RAxML-NG中实现了自适应树搜索启发式,根据预测的数据集难度修改了搜索强度.
- 利用机器学习对数据集难度的预测来指导自适应性搜索策略.
- 在9515个经验和5000个模拟的多个序列对齐 (MSAs) 上测试了适应式启发式,难度不同.
主要成果:
- 适应式启发式实现了实质性的加速度,特别是在容易和困难的数据集上 (53%的MSA),平均加速度超过10倍.
- 使用适应策略推断的约94%的树在统计学上与使用标准RAxML-NG策略获得的树无法区分.
- 适应性策略有效地利用数据集难度预测来优化树搜索效率,而不影响拓准确性.
结论:
- 适应式树搜索启发式 (adaptive tree search heuristic) 在计算效率上显著提高了族系推理的效率,特别是对于极其困难的数据集.
- 这种方法提供了一种实用的方法,可以通过智能地根据数据属性分配计算资源来加速家族遗传学分析.
- 适应性策略证明了将基于机器学习的难度预测集成到用于大规模的族群学研究的启发式搜索算法中的价值.
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