Beijing Institute of Lifeomics
Beijing, China, Beijing · 25 authors
Beijing Institute of Lifeomics (Beijing, China, Beijing): 25 authors and 22 papers in the de novo peptide sequencing catalog.
Departments
- National Center for Protein Sciences (Beijing)
- Research Unit of Proteomics Driven Cancer Precision Medicine, Chinese Academy of Medical Sciences
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing)
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing
Papers (22)
- π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection (2026, bioRxiv)
- 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)
- 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))
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning (2026, Nature Communications)
- A Comprehensive and Systematic Review for Deep Learning-Based De Novo Peptide Sequencing (2025, IJCAI 2025)
- DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning (2025, bioRxiv)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025, Nature Communications)
- NovoBench: Benchmarking Deep Learning-based De Novo Sequencing Methods in Proteomics (2024, NeurIPS 2024)
- Transforming de novo peptide sequencing by explainable AI (2024, Research Square)
- NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024, arXiv)
- 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, arXiv)
- 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)
- PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing (2023, Bioinformatics Advances)
- Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics (2019, Molecular & Cellular Proteomics)
- MRUniNovo: an efficient tool for de novo peptide sequencing utilizing the Hadoop distributed computing framework (2017, Bioinformatics)
- Algorithm Development of de novo Peptide Sequencing Via Tandem Mass Spectrometry (2010, Progress in Biochemistry and Biophysics)