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, MA, 02155, USA.
bioRxiv : the preprint server for biology
|November 26, 2025
概括
多视图投影 (MVP) 通过整合分子和光谱数据来增强代谢学. 这种新的框架提高了光谱注释率,推动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和疾病机制至关重要.
- 由于光谱复杂性的低分子结构分配率阻碍了代谢学方面的进步.
- 目前的方法很难有效地整合不同的数据视图来进行光谱注释.
研究的目的:
- 引入多视图投影 (MVP),这是一个新的框架用于在代谢学中的光谱注释.
- 开发一种学习分子和光谱之间的联合嵌入空间的方法.
- 为了提高分子结构识别从质谱数据的准确性和稳定性.
主要方法:
- MVP采用了对比的多视图学习,整合了分子图,指纹,光谱和共识光谱.
- 该框架通过在多个数据视图中捕获相互信息来学习联合嵌入空间.
- MVP支持使用个人或共识谱的灵活注释.
主要成果:
- MVP通过从所有数据视图中共同学习,显著提高了分子候选者排名.
- 使用MVP共识光谱的注释优于排名聚合策略.
- 使用共识光谱,MVP在基于大规模的检索中获得了35.99%的排名@1,在基于公式的检索中获得了13.96%.
结论:
- MVP提供了一种灵活和可扩展的基础,用于在代谢学中的光谱注释.
- 该框架增强了质谱学在疾病机制和药物开发研究方面的潜力.
- 通过MVP促进的更好的光谱注释率可以加速个性化医疗倡议.
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