Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac331.
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.
基于液相色谱-质谱（LC-MS）的非靶向代谢组学为代谢组学提供了系统的分析方法。然而，其在精准医学（疾病诊断）中的应用受到了一些挑战的限制，包括代谢物鉴定、信息丢失和低重现性。在这里，我们提出了基于深度学习的伪质谱成像（deepPseudoMSI）项目（https://www.deeppseudomsi.org/），它将 LC-MS 原始数据转换为伪 MS 图像，然后通过深度学习对其进行处理，用于精准医学，如疾病诊断。基于真实数据的广泛测试证明了 deepPseudoMSI 优于传统方法的优越性，以及我们的方法实现准确个体化诊断的能力。我们的框架为未来基于代谢的精准医学奠定了基础。
