MSMCE:一种用于分类原始质谱数据的新型表示模块
Fengyi Zhang1, Boyong Gao1, Yinchu Wang2,3,4
1China Jiliang University, College of Information Engineering, Hangzhou, China.
PloS one
|August 6, 2025
概括
本研究介绍了一种用于质谱 (MS) 数据的多通道嵌入表示模块 (MSMCE). 通过有效地捕获MS原始数据中的结构信息,MSMCE提高了分类性能.
科学领域:
- 生物医学数据分析
- 计算生物学是一种计算生物学.
- 频谱测量技术的使用方法
背景情况:
- 质谱 (MS) 在生物医学中至关重要,但由于高维和复杂的数据,它存在挑战.
- 目前用于MS分类的深度学习方法具有有限的特征表示,通常使用单通道数据,错过结构信息.
研究的目的:
- 开发一种新型模块,用于生成原始质谱数据的有效多通道表示.
- 改进质谱数据分析中的特征提取和分类性能.
主要方法:
- 提出了一个多通道嵌入式表示模块 (MSMCE),用于模拟MS原始数据的通道间依赖关系.
- 通过连接初始编码表示与多通道嵌入实现了特征融合机制.
- 在四个公共质谱数据集上对该模块进行了评估.
主要成果:
- 该MSMCE模块显著改善了MS原始数据的分类性能.
- 与现有方法相比,已证明提高了计算效率和培训稳定性.
- 通过多道表示,有效地捕获MS数据中的结构信息.
结论:
- 拟议的MSMCE模块为原始质谱数据分类提供了一个强大的方法.
- MSMCE提高了分类的准确性,效率和稳定性,显示了各种生物医学应用的潜力.
- 多通道表示是克服当前MS数据分析技术的局限性的关键.
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