Peng Cheng Laboratory
Shenzhen, China · 11 authors
Peng Cheng Laboratory (Shenzhen, China): 11 authors and 31 papers in the de novo peptide sequencing catalog.
Papers (31)
- π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection (2026, bioRxiv)
- High-accuracy glycan de novo prediction for N- and O-linked glycopeptides across multiple fragmentation techniques (2026, Nature Communications)
- De Novo Peptide Sequencing Daily Report (DNPS-DR) (2026, Hugging Face)
- 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)
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026, Nature Biotechnology)
- Mass spectrometry-based de novo sequencing reveals non-canonical neoantigens with antitumor efficacy in hepatocellular carcinoma (2026, JHEP Reports)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- Sequencing of Polyclonal Antibodies by Integrating Intact Mass, Middle–Down, and De Novo Bottom–Up Mass Spectrometry (2025, Molecular & Cellular Proteomics)
- Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025, Research Square)
- Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing (2024, arXiv)
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024, Molecular & Cellular Proteomics)
- Transforming de novo peptide sequencing by explainable AI (2024, Research Square)
- NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing (2024, bioRxiv)
- Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024, Briefings in Bioinformatics)
- A complete mass spectrometry-based immunopeptidomics pipeline for neoantigen identification and validation (2023, Research Square)
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023, Nature Machine Intelligence)
- Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023, bioRxiv)
- Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics (2023, Nature Communications)
- Discovering and Validating Neoantigens by Mass Spectrometry-based Immunopeptidomics and Deep Learning (2022, bioRxiv)
- A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics (2022, Nature Communications)
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021, Nature Machine Intelligence)
- Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines (2020, Nature Machine Intelligence)
- Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines (2019, bioRxiv)
- DeepNovoV2: Better de novo peptide sequencing with deep learning (2019, arXiv)
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018, Nature Methods)
- Protein identification with deep learning: from abc to xyz (2017, arXiv)
- De novo peptide sequencing by deep learning (2017, Proceedings of the National Academy of Sciences)
- Complete De Novo Assembly of Monoclonal Antibody Sequences (2016, Scientific Reports)
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003, Rapid Communications in Mass Spectrometry)