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Targeted proteomics data interpretation with DeepMRM
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DOI：10.1016/j.crmeth.2023.100521.png)
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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 detec-tion developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evalu-ated on internal and public datasets, demonstrating superior accuracy compared with the community stan-dard 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.
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