揭示了基于拉曼光谱的大型模型在预测微生物单细胞拉曼光谱中的有效性
Thomas J Tewes1, Mario Kerst1, Svyatoslav Pavlov1
1Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany.
Heliyon
|March 21, 2024
概括
使用批量数据预测单个微生物细胞的拉曼光谱是有条件有效的. 虽然一些物种表现出高精度,但由于光和光降解等因素,其他物种具有挑战性.
科学领域:
- 微生物学 微生物学
- 频谱学是一种光谱学.
- 计算生物学 计算生物学
背景情况:
- 之前的工作开发了不同基板上的微生物拉曼光谱的预测模型.
- 卷积神经网络 (CNN) 为光谱数据提供先进的模式识别功能.
研究的目的:
- 研究使用大量微生物拉曼光谱数据来训练单细胞拉曼光谱预测模型的可行性.
- 评估这些跨数据集预测的准确性和局限性.
主要方法:
- 结合了不同基板上的微生物现有的拉曼光谱数据集.
- 使用大量微生物拉曼光谱训练了一个卷积神经网络 (CNN).
- 在13种不同的微生物 (细菌和酵母) 的单细胞拉曼光谱上测试了CNN的预测性能.
主要成果:
- 在13种微生物物种中,有2种被识别出,准确度为90%.
- 另外五种物种实现了71%-88%的正确预测率.
- 六种物种的预测准确度较低 (0%-49%),受光和光降解等因素的影响.
结论:
- 大量微生物拉曼光谱的数据库有条件适用于预测单细胞光谱.
- 大量和单细胞样本之间的光和光降解的差异可能会影响预测的准确性.
- 这些发现有助于开发通用拉曼光谱工具和微生物识别数据库.
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