State Key Laboratory of Medical Proteomics
Beijing, China · 8 authors
State Key Laboratory of Medical Proteomics (Beijing, China): 8 authors and 16 papers in the de novo peptide sequencing catalog.
Papers (16)
- AI proteomics: from protein identification to virtual cells (2026, Nature Methods)
- False discovery rate control for trustworthy AI-based de novo peptide sequencing (2026, bioRxiv)
- π-HelixNovo2: Making Accurate Online De Novo Peptide Sequencing Available to All (2026, Genomics, Proteomics & Bioinformatics)
- A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data (2026, Nature Machine Intelligence)
- π-MSNet: A billion-scale, AI-ready living proteomics data portal (2026, bioRxiv)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- A Comprehensive and Systematic Review for Deep Learning-Based De Novo Peptide Sequencing (2025, IJCAI 2025)
- pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation (2025, arXiv)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025, Nature Communications)
- Transforming de novo peptide sequencing by explainable AI (2024, Research Square)
- NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024, NeurIPS 2024)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024, bioRxiv)
- ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing (2024, AAAI 2024)
- AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024, ICML 2024)
- Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024, Briefings in Bioinformatics)
- Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023, bioRxiv)