深度学习方法对已灭绝的花粉形态类型的遗传学定位进行了研究
Marc-Élie Adaïmé1, Shu Kong2,3, Surangi W Punyasena1
1Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
PNAS nexus
|January 11, 2024
概括
深度学习从花粉形态学解读了进化史. 这种新的方法使用神经网络来分析花粉特征,使化石标本能够准确地排列,并揭示了过去的灭绝事件.
科学领域:
- 古植物学是古植物学.
- 计算生物学 计算生物学
- 进化生物学 进化生物学
背景情况:
- 对化石花粉的遗传学解释是具有挑战性的,因为在花粉形态学中对进化历史的理解有限.
- 传统的方法很难从化石花粉颗粒中提取详细的进化信息.
研究的目的:
- 开发一种深度学习工具包,用于分析花粉形态和推断家族遗传关系.
- 利用神经网络从化石花粉数据重建进化历史.
- 为了提高化石花粉记录的分类分辨率.
主要方法:
- 使用神经网络开发了一套类遗传学工具包,用于分析花粉形状,内部结构和纹理.
- 实施了分析管道,以确定标本的分类学确定性.
- 采用多层感知子网络来预测不确定的标本的遗传学距离.
- 用贝叶斯推断来确定化石花粉样本的遗传学位置.
- 经过训练和验证的模型使用光学超分辨率显微镜对30种现存的Podocarpus物种进行了测试.
主要成果:
- 成功训练神经网络以识别花粉形态中编码的家族遗传历史.
- 证明了深度学习特征对化石 * Podocarpidites * 标本的遗传学定位的有用性.
- 将九个化石标本置于 * Podocarpus * 族群中,提高了分类学分辨率.
结论:
- 深度学习可以有效地从花粉形态学中提取和解释进化信息.
- 开发的工具包使得化石花粉的遗传学定位成为可能,揭示了以前隐藏的灭绝和物种化事件.
- 这种方法通过克服化石花粉分类学分辨率的局限性,大大推进了古生物学分析.
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