Institute of Computing Technology
Beijing, China · 2 authors
Institute of Computing Technology (Beijing, China): 2 authors and 11 papers in the de novo peptide sequencing catalog.
Papers (11)
- A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data (2026, Nature Machine Intelligence)
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning (2026, Nature Communications)
- 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)
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019, Bioinformatics)
- Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics (2019, Molecular & Cellular Proteomics)
- pSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization (2017, Journal of Proteome Research)
- Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications (2017, Journal of Proteome Research)
- De novo identification and quantification of single amino-acid variants in human brain (2014, Journal of Molecular Cell Biology)
- 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)