Ming Li

18 papers in the catalog · Canada, China

Ming Li: 18 papers in the catalog · Canada, China · Bioinformatics Solutions Inc., Peng Cheng Laboratory · Works on AI proteomics perspective, DIANovo, Deep-learning protein identification primer

Affiliations

Bioinformatics Solutions Inc.
Peng Cheng Laboratory
University of Waterloo · David R. Cheriton School of Computer Science
University of Waterloo · School of Computer Science
University of Western Ontario · Department of Computer Science

Papers

AI proteomics: from protein identification to virtual cells (2026, Nature Methods)
Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026, Nature Biotechnology)
A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
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)
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)
Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023, Nature Machine Intelligence)
Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics (2023, 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, PNAS)
PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003, Rapid Communications in Mass Spectrometry)

Methods and tools

AI proteomics perspective, DIANovo, Deep-learning protein identification primer, DeepNovo, DeepNovo Peptidome, DeepNovo V2, DeepNovo-DIA, DeepNovoAA, GlycanFinder, GraphNovo, Living proteomics benchmark, NovoBoard, PEAKS, PointNovo, RNovA

Co-authors (144)

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 2.54, 16 shared
Baozhen Shan strength 2.06, 14 shared
Ngoc Hieu Tran strength 1.80, 11 shared
Rui Qiao strength 1.20, 8 shared
Zeping Mao strength 0.97, 6 shared
Xin Chen strength 0.69, 4 shared
Ali Ghodsi strength 0.63, 4 shared
Ruixue Zhang strength 0.58, 3 shared
Chao Peng strength 0.40, 4 shared
Xianglilan Zhang strength 0.38, 2 shared
Qianqiu Zhang strength 0.34, 3 shared
Qing Zhang strength 0.31, 3 shared
Wenting Li strength 0.31, 3 shared
Ping Wu strength 0.30, 3 shared
Shengying Pan strength 0.25, 2 shared
Zachariah Levine strength 0.25, 1 shared
Haiteng Deng strength 0.24, 2 shared
Yuling Chen strength 0.24, 2 shared
Bin Ma strength 0.19, 2 shared
Amanda Doherty-Kirby strength 0.17, 1 shared
Chengzhi Liang strength 0.17, 1 shared
Christopher Hendrie strength 0.17, 1 shared
Gilles Lajoie strength 0.17, 1 shared
Kaizhong Zhang strength 0.17, 1 shared
Jiazhen Chen strength 0.14, 1 shared
Zheng Ma strength 0.14, 1 shared
Chuyi Liu strength 0.12, 1 shared
Jun Ma strength 0.10, 1 shared
M. Ziaur Rahman strength 0.10, 1 shared
Weiping Sun strength 0.10, 1 shared
Xiyue Zhang strength 0.10, 1 shared
Yonghan Yu strength 0.10, 1 shared
Zheng Chen strength 0.10, 1 shared
Chungong Yu strength 0.06, 1 shared
Dongbo Bu strength 0.06, 1 shared
Haicang Zhang strength 0.06, 1 shared
Haiming Qin strength 0.06, 1 shared
Haofei Miao strength 0.06, 1 shared
Jingxiang Lang strength 0.06, 1 shared
Qingyang Lei strength 0.06, 1 shared

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