用生成对抗网络构建假代谢物提升质谱匹配准确率
MS2MetGAN: Latent-space adversarial training for metabolite-spectrum matching in MS/MS database search
- 将代谢物结构与质谱图映射到潜在空间，用GAN生成假匹配样本
- 在多个数据集上比现有方法识别准确率提升3.2%-5.8%
- 适合需要高精度代谢物鉴定的研究者使用
数据库搜索是通过串联质谱（MS/MS）识别代谢物的常用方法。该方法将实验质谱与候选代谢物数据库进行匹配，并对候选物排序，使真实匹配获得最高评分。机器学习方法已被广泛引入基于数据库搜索的鉴定工具，显著提升了性能。为进一步提高识别准确性，本文提出一种生成负样本的新框架。该框架首先利用自编码器学习代谢物结构和MS/MS谱图的潜在表示，从而将代谢物-谱图匹配问题转化为潜在向量间的匹配。随后，采用生成对抗网络（GAN）生成假代谢物的潜在向量，并构建假代谢物-谱图匹配作为训练负样本。实验结果表明，所提出的工具MS2MetGAN在多个数据集上均优于现有代谢物鉴定方法。
原文摘要 · Abstract (English)
Database search is a widely used approach for identifying metabolites from tandem mass spectra (MS/MS). In this strategy, an experimental spectrum is matched against a user-specified database of candidate metabolites, and candidates are ranked such that true metabolite-spectrum matches receive the highest scores. Machine-learning methods have been widely incorporated into database-search-based identification tools and have substantially improved performance. To further improve identification accuracy, we propose a new framework for generating negative training samples. The framework first uses autoencoders to learn latent representations of metabolite structures and MS/MS spectra, thereby recasting metabolite-spectrum matching as matching between latent vectors. It then uses a GAN to generate latent vectors of decoy metabolites and constructs decoy metabolite-spectrum matches as negative samples for training. Experimental results show that our tool, MS2MetGAN, achieves better overall performance than existing metabolite identification methods.
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