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1. Expanding the Use of Spectral Libraries in Proteomics

2. Challenging the Astral mass analyzer - going beyond 5200 proteins per single-cell at unseen quantitative accuracy to study cellular heterogeneity.

3. Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

4. Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

7. Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides

11. A Novel Spectral Annotation Strategy Streamlines Reporting of mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ

12. INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results

13. Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

14. A Novel Lipidomics Workflow for Improved Human Plasma Identification and Quantification Using RPLC-MSn Methods and Isotope Dilution Strategies

15. Building ProteomeTools based on a complete synthetic human proteome

18. The Escherichia coli Phosphotyrosine Proteome Relates to Core Pathways and Virulence

20. Comprehensive proteomic analysis of human bile

22. Quantitative temporal proteomic analysis of human embryonic stem cell differentiation into oligodendrocyte progenitor cells

23. A proteogenomic analysis of Anopheles gambiae using high-resolution Fourier transform mass spectrometry

25. A Comprehensive Map of the Human Urinary Proteome

26. The Escherichia coli Phosphotyrosine Proteome Relates to Core Pathways and Virulence.

27. A Novel Spectral Annotation Strategy Streamlines Reporting of Mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ.

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