Expanding the coverage of spatial proteomics: a machine learning approach.

Journal: Bioinformatics (Oxford, England)
Published Date: Feb 1, 2024

Abstract

MOTIVATION: Multiplexed protein imaging methods use a chosen set of markers and provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is inherently limited.

Authors

  • Huangqingbo Sun
    Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Jiayi Li
    Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095-1554, USA.
  • Robert F Murphy
    Computational Biology Department, Center for Bioimage Informatics, and Departments of Biological Sciences, Biomedical Engineering and Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA; Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, Germany. Electronic address: murphy@cmu.edu.

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