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深度学习和病毒类地质学的概率方法汇聚在相同的答案上,无论推理模型是正确还是错误
Ammon Thompson1, Benjamin J Liebeskind2, Erik J Scully2
1Participant in an Education Program Sponsored by U.S. Department of Defense (DOD) at the National Geospatial-Intelligence Agency, Springfield, VA 22150, USA.
Systematic biology
|January 8, 2024
概括
深度学习方法为流行病学提供了一个更快,更准确的替代传统的家族遗传树分析方法. 这些神经网络有效地推断出病毒传播动态,甚至在速度和可比的稳定性方面超过了已建立的贝叶斯方法.
科学领域:
- 计算生物学 计算生物学
- 流行病学 流行病学
- 机器学习 机器学习
背景情况:
- 遗传树分析对于理解病毒传播动态和流行病学至关重要.
- 传统的基于概率的家族遗传方法是计算密集的,限制了实时爆发分析.
- 深度学习提供了一个有前途的途径,用于计算效率高,从家族遗传数据中推断无概率的推断.
研究的目的:
- 扩展,比较和对比一个基于深度学习的无概率推断方法,用于家族遗传树.
- 与传统方法相比,评估深度神经网络的准确性,稳定性和计算效率.
- 将开发的深度学习方法应用于现实世界的生物地理数据,例如SARS-CoV-2流行病.
主要方法:
- 训练了多个深度神经网络,使用在五个地点模拟的爆发类型.
- 将性能和稳定性与神经网络的模型错误规范与贝叶斯推理进行比较.
- 实现了对不确定性量化的形式化定量回归,并将该方法应用于SARS-CoV-2的生物地理数据.
主要成果:
- 深度神经网络在模拟数据上的准确度与贝叶斯推理相当.
- 神经网络和贝叶斯方法都在面对模型错误规范时表现出类似的偏见.
- 深度学习方法在训练后比传统方法快3个数量级,在SARS-CoV-2数据上具有可比的稳定性和准确性.
结论:
- 深度学习为流行病学中的遗传学树分析提供了一个计算效率高,准确的替代方案.
- 训练有素的神经网络可以有效地模仿基于概率的家族遗传模型的统计特性.
- 这种方法在快速发展的疫情期间具有实时政策制定的巨大潜力.
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