University of Chinese Academy of Sciences
Beijing, China · 30 authors
University of Chinese Academy of Sciences (Beijing, China): 30 authors and 14 papers in the de novo peptide sequencing catalog.
Papers (14)
- Accurate and ultra-fast de novo HLA-I immunopeptide sequencing with FoxNovo (2026, LangTaoSha (LTS) Preprint)
- AI proteomics: from protein identification to virtual cells (2026, Nature Methods)
- A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data (2026, Nature Machine Intelligence)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation (2025, arXiv)
- DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning (2025, bioRxiv)
- Denovo-GCN: De Novo Peptide Sequencing by Graph Convolutional Neural Networks (2023, Applied Sciences (MDPI))
- Deep Learning in Proteomics (2020, Proteomics)
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019, Bioinformatics)
- pSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization (2017, Journal of Proteome Research)
- NIPTL-Novo: Non-isobaric peptide termini labeling assisted peptide de novo sequencing (2017, Journal of Proteomics)
- Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications (2017, Journal of Proteome Research)
- pNovo+: De Novo Peptide Sequencing Using Complementary HCD and ETD Tandem Mass Spectra (2013, Journal of Proteome Research)
- pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra (2010, Journal of Proteome Research)