一个新的变压器-MLP融合网络,用于从质谱中识别代谢物
Xiaofeng Zhang1, Ming Yan1, Yian Liu1
1College of Automation, Hangzhou Dianzi University, Hangzhou, 310028, China.
Talanta
|November 22, 2025
概括
一个新的特征融合网络 (FFNet) 改善了从质谱中的代谢物识别. 这种人工智能模型准确地预测分子指纹,有助于发现用于非目标代谢的新型化合物.
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:
- 准确的代谢物识别对于非目标代谢学至关重要.
- 识别从光谱库中缺少的新型化合物是一个重大挑战.
研究的目的:
- 开发一种新的深度学习模型,即特征融合网络 (FFNet),用于从质谱中增强代谢物识别.
- 使用双流架构从质谱中预测分子指纹.
主要方法:
- 开发了FFNet,这是一个双流网络,结合了变压器和多层感知器 (MLP) 路径.
- 集成的全球光谱环境 (变压器) 和局部光谱特征 (MLP) 通过一个细心的融合机制.
- 在GMIS,MoNA和CASMI 2022数据集上评估FFNet,用于指纹预测和代谢物识别.
主要成果:
- 在指纹预测和代谢物识别任务中,FFNet的表现优于基线神经网络模型.
- 在大型数据集 (GMIS,MoNA) 上表现出卓越的性能.
- 在结构阐明实验中成功检索了未知化合物的结构相似的候选分子.
结论:
- 神经特征融合显著增强了质谱分析.
- FFNet使得更可靠的代谢物识别成为可能,特别是对于新型化合物.
- 拟议的方法提高了复杂的生物样本分析能力.
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