DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation
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DOI: 10.1038/s41467-021-26979-1
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References listed on IDEAS
- Brian C. Searle & Kristian E. Swearingen & Christopher A. Barnes & Tobias Schmidt & Siegfried Gessulat & Bernhard Küster & Mathias Wilhelm, 2020. "Generating high quality libraries for DIA MS with empirically corrected peptide predictions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
- Yi Yang & Xiaohui Liu & Chengpin Shen & Yu Lin & Pengyuan Yang & Liang Qiao, 2020. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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Cited by:
- Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Michael A. Skinnider & Mopelola O. Akinlaja & Leonard J. Foster, 2023. "Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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