从所有视角学习:一种多视角对比框架用于代谢物注释
Yan Zhou Chen1, Soha Hassoun1,2
1Department of Computer Science, Tufts University, Medford, Massachusetts 02155, United States.
Analytical chemistry
|February 23, 2026
概括
一个新的框架,多视图投影 (MVP),通过联合分析分子和光谱数据来增强代谢物识别. 这种方法提高了光谱注释的准确性,促进了疾病研究和药物发现.
科学领域:
- 代谢学 代谢学 代谢学
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 使用高通量质谱的代谢学对于理解细胞生物化学,疾病机制,药物开发和个性化医学至关重要.
- 低的光谱注释率是由于将分子结构分配到测量的光谱的挑战,阻碍了代谢学方面的进步.
研究的目的:
- 引入多视图投影 (MVP),这是一个新的框架,旨在提高在代谢学中的光谱注释率.
- 通过整合多个数据视图,为分子和光谱创建一个联合嵌入空间.
主要方法:
- MVP使用对比的多视图学习来捕捉不同数据视图的相互信息:分子图,分子指纹,光谱和共识光谱.
- 该框架从所有视图中共同学习,与以前使用连接或辅助任务的方法不同.
- MVP支持使用个人频谱或共识频谱的灵活注释.
主要成果:
- MVP显著改善了用于光谱注释的分子候选排名.
- 使用MVP的查询共识光谱进行注释,优于基于构成频谱注释的排名聚合策略.
- 在使用共识光谱进行基于公式的检索中,MVP 实现了 36.0% 的 rank@1 量级检索和 14.0%.
结论:
- MVP提供了一个灵活和可扩展的基础,用于从多个分子/光谱数据视图中学习.
- 该框架的性能优于或与现有的光谱注释方法相提并论.
- 通过MVP促进的更好的注释率可以加速在代谢学相关领域的发现.
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