Targeted proteomics data interpretation with DeepMRM

Jungkap Park, Christopher Wilkins, Dmitry Avtonomov, Jiwon Hong, Seunghoon Back, Hokeun Kim, Nicholas Shulman, Brendan X. MacLean, Sang Won Lee, Sangtae Kim

Research output: Contribution to journalArticlepeer-review


Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.

Original languageEnglish
Article number100521
JournalCell Reports Methods
Issue number7
Publication statusPublished - 2023 Jul 24

Bibliographical note

Publisher Copyright:
© 2023 The Authors


  • CP: Systems biology
  • Skyline
  • machine learning
  • multiple reaction monitoring
  • object detection
  • peak detection
  • quality control
  • quantification
  • selected reaction monitoring
  • targeted proteomics

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Genetics
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


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