混NN:在人口基因组学中解释卷积神经网络推断,使用数据混
Linh N Tran1,2, David Castellano2, Ryan N Gutenkunst2
1Genetics Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA.
bioRxiv : the preprint server for biology
|April 8, 2025
概括
ConfuseNN有助于理解基因组特征如何影响人口遗传学的卷积神经网络 (CNN). 这种方法揭示了哪些遗传模式对CNN在推断人口历史和选择中的准确性至关重要.
科学领域:
- 人口遗传学 人口遗传学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 卷积神经网络 (CNN) 越来越多地用于人口基因组推断.
- 然而,驱动CNN业绩的特定基因组特征尚未得到充分理解.
研究的目的:
- 介绍ConfuseNN,一种用于解释CNN在人口遗传学中的表现的新方法.
- 系统地评估不同人口遗传特征对CNN准确性的贡献.
主要方法:
- 混NN系统地混合输入的单双型矩阵,以破坏特定的人口遗传特征.
- 该方法连续删除诸如链接不平衡和等位基因频率等信号,以评估它们对CNN性能的影响.
- 将ConfuseNN应用于现有的CNN以进行人口历史和选择推断.
主要成果:
- 确认了特定基因组特征对CNN在人口遗传推断中的表现的重要性.
- 确定了当前CNN架构和模拟训练/测试数据设计的局限性.
- 证明了ConfuseNN在评估各种人口遗传学任务中的CNN的实用性.
结论:
- 在人口遗传学中,ConfuseNN为解释CNN提供了一个生物动机的框架.
- 这种方法有助于弥合深度学习方法与传统人口遗传理论之间的差距.
- 有助于更深入地了解CNN如何从基因组数据中学习.
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