Baozhen Shan

26 papers in the catalog · Canada

Baozhen Shan: 26 papers in the catalog · Canada · Bioinformatics Solutions (Canada), Bioinformatics Solutions Inc. · Works on ALPS, Champs, DIANovo

Affiliations

• Bioinformatics Solutions (Canada)
• Bioinformatics Solutions Inc.
• Western University

Elsewhere

ORCID 0000-0002-8757-5493 · OpenAlex

Papers

• High-accuracy glycan de novo prediction for N- and O-linked glycopeptides across multiple fragmentation techniques (2026, Nature Communications)
• 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)
• De Novo sequencing-assisted homology search for DIA data analysis enables low abundance peptide variants discovery (2025, bioRxiv)
• 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)
• NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing (2024, bioRxiv)
• A complete mass spectrometry-based immunopeptidomics pipeline for neoantigen identification and validation (2023, Research Square)
• Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics (2023, Nature Communications)
• De novo sequencing of multiple SILAC-based tandem mass spectra (2022, 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC))
• 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)
• Integrating de novo Sequencing and Database Search for Monoclonal Antibody Sequencing (2013, Europe PMC (PubMed Central))
• PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2012, Molecular & Cellular Proteomics)
• Integrating de Novo Sequencing and Database Search for Peptide Identification (2012, Europe PMC (PubMed Central))
• Better score function for peptide identification with ETD MS/MS spectra (2010, BMC Bioinformatics)

Methods and tools

ALPS, Champs, DIANovo, DIAVariant, Deep-learning protein identification primer, DeepGlycan, DeepNovo, DeepNovo Peptidome, DeepNovo V2, DeepNovo-DIA, DeepNovoAA, GlycanFinder, HCC non-canonical neoantigen discovery, Living proteomics benchmark, NovoBoard, PEAKS, PEAKS DB, PointNovo, PolySeq.AI, RNovA, SILAC group de novo

Co-authors (114)

Ranked by Newman fractional collaboration strength, so a pair on a two-author paper counts for more than a pair on a 50-author consortium paper.

• Lei Xin strength 3.69, 22 shared
• Ming Li strength 2.82, 20 shared
• Ngoc Hieu Tran strength 2.23, 15 shared
• Rui Qiao strength 1.61, 11 shared
• Xin Lei strength 1.00, 1 shared
• Bin Ma strength 0.96, 4 shared
• Xin Chen (Waterloo) strength 0.79, 5 shared
• Zeping Mao strength 0.69, 5 shared
• Ali Ghodsi strength 0.63, 4 shared
• Fang Han strength 0.50, 1 shared
• Kaizhong Zhang strength 0.50, 1 shared
• Wenting Li strength 0.44, 5 shared
• Qianqiu Zhang strength 0.40, 3 shared
• Chao Peng strength 0.39, 5 shared
• Xianglilan Zhang strength 0.38, 2 shared
• Qing Zhang strength 0.37, 4 shared
• Haibo Bian strength 0.35, 2 shared
• Shengying Pan strength 0.35, 3 shared
• Xiaowen Liu strength 0.33, 1 shared
• Haiteng Deng strength 0.30, 2 shared
• M. Ziaur Rahman strength 0.30, 2 shared
• Yuling Chen strength 0.30, 2 shared
• Hechen Li strength 0.25, 1 shared
• Zachariah Levine strength 0.25, 1 shared
• Ruixue Zhang strength 0.24, 2 shared
• Xiaolong Liu strength 0.24, 3 shared
• Ping Wu strength 0.22, 3 shared
• Lin He strength 0.20, 1 shared
• Jun Ma strength 0.17, 2 shared
• Weiping Sun strength 0.17, 2 shared
• Xiyue Zhang strength 0.17, 2 shared
• Zheng Chen strength 0.17, 2 shared
• Jiazhen Chen strength 0.14, 1 shared
• Zheng Ma strength 0.14, 1 shared
• Chuyi Liu strength 0.12, 1 shared
• En Hu strength 0.12, 1 shared
• Jiahe Ouyang strength 0.12, 1 shared
• Mingxin Liu strength 0.12, 1 shared
• Ruijing Tang strength 0.12, 1 shared
• Xiaohua Xing strength 0.12, 1 shared

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