Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine.

Journal: Briefings in bioinformatics
Published Date: Sep 20, 2022

Abstract

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.

Authors

  • Xiaotao Shen
    Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Wei Shao
  • Chuchu Wang
    Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
  • Liang Liang
    Department of Computer Science, University of Miami, Coral Gables, FL.
  • Songjie Chen
    Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Sai Zhang
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
  • Mirabela Rusu
    Department of Radiology, Stanford University, Stanford, CA 94305, USA. Electronic address: mirabela.rusu@stanford.edu.
  • Michael P Snyder
    Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA.

Keywords

External Resources

Popular Topics
Recent Journals