使用质谱进行非靶向代谢组学分析可提供全面的代谢分析，但其医学应用面临着数据处理复杂、批次间变异性高和代谢物不明的挑战。在这里，我们提出了 DeepMSProfiler，这是一种可解释的基于深度学习的方法，能够对原始代谢信号进行端到端分析，并输出高精度和可靠性。使用来自肺腺癌、良性肺结节和健康个体的跨医院 859 份人血清样本，DeepMSProfiler 成功区分不同群体的代谢组图谱（AUC 0.99）并检测早期肺腺癌（准确度 0.961）。模型流和消融实验表明 DeepMSProfiler 克服了医院间的变异性和未知代谢物信号的影响。我们的集成策略消除了多分类深度学习模型中的背景类别现象，并且新颖的可解释性使得能够直接访问与疾病相关的代谢物蛋白质网络。进一步应用于脂质代谢组数据揭示了重要代谢物和蛋白质的相关性。总体而言，DeepMSProfiler 为疾病诊断和机制发现提供了一种简单可靠的方法，增强了其广泛的适用性。© 2024。作者。

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.© 2024. The Author(s).