创建可解释的深度学习模型以使用环境DNA序列识别物种
Samuel Waggoner1, Jon Donnelly2, Rose Gurung3
1School of Computing and Information Science, University of Maine, Orono, 04469, USA. samuel.waggoner@maine.edu.
Scientific reports
|July 28, 2025
概括
本研究引入了一个可解释的深度学习模型,用于环境DNA (eDNA) 物种识别. 新方法提高了准确性,并提供了视觉解释,改进了传统和黑子CNN方法.
科学领域:
- 生态与保护生物学 生态与保护生物学
- 生物信息学和计算生物学
- 机器学习和人工智能的人工智能
背景情况:
- 监测物种存在对于生态系统保护和息地评估至关重要.
- 环境DNA (eDNA) 分析为物种检测的传统方法提供了具有成本效益的替代方案.
- 目前的深度学习方法,如卷积神经网络 (CNN) 是快速的,但缺乏可解释性.
研究的目的:
- 为eDNA物种识别开发一个可解释的深度学习框架.
- 为了提高基于CNN的eDNA分析的准确性和透明度.
- 可视化与特定物种相关的独特DNA序列.
主要方法:
- 利用ProtoPNet框架创建一个基于原型的,可解释的CNN.
- 引入了一种新的跳过连接,以提高原始ProtoPNet的解释性.
- 在一个具有挑战性的eDNA数据集上评估模型,将其性能与现有方法进行比较.
主要成果:
- 可解释的CNN在eDNA数据集上实现了比以前的方法更高的准确性.
- 该模型成功地可视化了特定物种的DNA基序,有助于"事实检查"预测.
- 减少对卷积输出的依赖,提高了模型的解释性和预测准确性.
结论:
- 一个可解释的,基于原型的CNN (ProtoPNet) 在eDNA分析方面取得了重大进展.
- 可视化独特的DNA序列可以提高生态学中的深度学习模型的透明度和可靠性.
- 这种方法有望实现更准确,更易于理解的生物多样性监测.
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