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从样本图像中提取的形态特征推断分类学亲和关系和遗传距离:用双数据集进行案例研究

Martin Hofmann¹, Steffen Kiel², Lara M Kösters³

¹Data-intensive Systems and Visualization Group (dAI.SY), Technical University Ilmenau, Ilmenau 98693, Germany.

Systematic biology

|July 24, 2024

概括

深度学习模型可以从双标本图像中推断进化关系,将视觉相似性与遗传数据相关联. 这种方法提供了一种新的方法,用于在没有分子数据的情况下进行遗传学分析.

科学领域:

* 进化生物学和系统生物学.
* 计算生物学和生物信息学.

背景情况:

*重建生命树和理解分类关系是进化生物学的核心.
*分子遗传学推动了进步,但对于大多数物种来说,分子数据是不可用的.
* 样本图像提供了一个潜在的数据来源,用于遗传学推断.

研究的目的:

* 探索深度学习方法的适用性,以从样本图像中推断生物体关系.
* 评估双动物视觉相似性和遗传关系之间的相关性.
* 开发自动化分类型识别系统和新的家族遗传学估计方法.

主要方法:

* 收集了4144种双动物的大量图像数据集,并提供了分子遗传学数据和分类层次结构.
* 员工监督的分类与分类层次和遗传距离用于多层次的预测.
* 利用转移学习和相似性学习来实现未知物种的零射击识别.
* 应用无监督相似性学习,从图像中推断相关性,而没有先前的分类学知识.

主要成果:

* 深度学习模型在物种级别识别中实现了近80%的准确性.
*零射击学习模型以48-67%的准确度确定了更高层次的分类学亲和关系.
*无监督学习揭示了视觉外观与更高分类层的遗传关系之间的显著相关性 (家族层相关性:0.78).
*视觉相似性与遗传距离相关,特别是在物种丰富的类和子类中.

结论:

* 深度学习方法可以有效地从样本图像中推断家族遗传关系,补充分子数据.
*双动物图像中的视觉相似性反映了遗传关系,特别是在更高的分类学等级.
* 这项研究扩大了自动识别系统的实用性,并为遗传学分析提供了一种新的方法.

关键词:

双类动物双类动物深度学习是一种深度学习.形态学推断推断的结论人类遗传学是个学科.相似性学习学习的相似性学习

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