马斯伯特:通过面具学习和对比学习将协同质谱嵌入到化学理性空间
Hailiang Zhang1, Qiong Yang1, Ting Xie1
1College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
Analytical chemistry
|October 14, 2024
概括
采用自主监督学习的新方法MSBERT有效地从双重质谱 (MS/MS) 数据中识别化合物. 它在光谱图书馆匹配和类似的化合物搜索方面表现优于现有的技术.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 双重质谱 (MS/MS) 产生了大量的光谱数据,对于化合物识别至关重要.
- 液体染色学-质谱学 (LC-MS) 实验产生了需要高效分析的大数据集.
- 目前用于MS/MS光谱解释的方法在速度和准确性方面面临挑战.
研究的目的:
- 利用MS/MS光谱数据开发一种有效的化合物识别方法.
- 通过自我监督学习,为MS/MS频谱创造有效的嵌入.
- 增强光谱图书馆匹配和类似化合物搜索的能力.
主要方法:
- 提出了MSBERT,这是一个基于自我监督学习和变压器编码器架构的模型.
- 在MS/MS频谱上利用了面具学习和对比学习策略.
- 在各种数据集上训练并测试MSBERT,包括GNPS,MoNA和MTBLS1572.
主要成果:
- 在图书馆匹配和类似的复合搜索中,MSBERT表现出卓越的性能.
- 与Spec2Vec和DreaMS相比,实现了更高的召回率 (0.7871,0.8950,0.9080在1,5,10) 和Spec2Vec和DreaMS.
- 通过t-SNE可视化,结构相似性和集群分析验证了嵌入理性.
结论:
- MSBERT提供了从MS/MS数据中增强的化合物识别功能.
- 开发的嵌入式是合理的,并改进了光谱分析.
- 为了可访问性,提供了一个用户友好的Web服务器和开源代码.
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