Hao Chi

11 papers in the catalog · China, Shanghai, Hangzhou

Hao Chi: 11 papers in the catalog · China, Shanghai, Hangzhou · Chinese Academy of Sciences, Chinese Academy of Sciences · Works on De novo SAV discovery, DiNovo, Open-pNovo

Affiliations

• Chinese Academy of Sciences
• Chinese Academy of Sciences · Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology
• Institute of Computing Technology
• University of Chinese Academy of Sciences

Elsewhere

ORCID 0000-0002-7898-2599 · OpenAlex

Papers

• 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)

Methods and tools

De novo SAV discovery, DiNovo, Open-pNovo, pNovo, pNovo 3, pNovo+, pNovoM, pSite, pUniFind

Co-authors (68)

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.

• Si-Min He strength 0.88, 7 shared
• Wen-Feng Zeng strength 0.68, 5 shared
• Hao Yang strength 0.58, 4 shared
• Wen-Jing Zhou strength 0.50, 3 shared
• Chao Liu strength 0.35, 3 shared
• Rui-Xiang Sun strength 0.34, 3 shared
• Kun He strength 0.24, 2 shared
• Haipeng Wang strength 0.22, 3 shared
• Yan Fu strength 0.22, 3 shared
• Bing Yang strength 0.20, 2 shared
• Chun-Qing Song strength 0.20, 2 shared
• Meng-Qiu Dong strength 0.20, 2 shared
• Ping Xu strength 0.19, 3 shared
• Yanchang Li strength 0.19, 3 shared
• Han Wen strength 0.14, 2 shared
• Jiale Zhao strength 0.14, 2 shared
• Jiaxiang Ding strength 0.14, 2 shared
• Kaifei Wang strength 0.14, 2 shared
• Pengzhi Mao strength 0.14, 2 shared
• Ranfei Chen strength 0.14, 2 shared
• Shuqi Lu strength 0.14, 2 shared
• Weijie Zhang strength 0.14, 2 shared
• Weinan E strength 0.14, 2 shared
• Xiaohong Ji strength 0.14, 2 shared
• Xin Zhang strength 0.14, 2 shared
• Yaping Peng strength 0.14, 2 shared
• Yiming Li strength 0.14, 2 shared
• Yucheng Liao strength 0.14, 2 shared
• Di Zhang strength 0.12, 2 shared
• Jiaxing Dai strength 0.12, 2 shared
• Jinghan Yang strength 0.12, 2 shared
• Li Kang strength 0.12, 2 shared
• Lijin Yao strength 0.12, 2 shared
• Piyu Zhou strength 0.12, 2 shared
• Ruitao Wu strength 0.12, 2 shared
• Xinming Li strength 0.12, 2 shared
• Xueli Peng strength 0.12, 2 shared
• Yao Zhang strength 0.12, 2 shared
• Zhiyuan Cheng strength 0.12, 2 shared
• Zixuan Cao strength 0.12, 2 shared

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