Cheng Chang

11 papers in the catalog · China

Cheng Chang: 11 papers in the catalog · China · Beijing Institute of Lifeomics, Beijing Institute of Lifeomics · Works on AI proteomics perspective, ContraNovo, FDR control for AI-based de novo sequencing

Affiliations

Beijing Institute of Lifeomics · National Center for Protein Sciences (Beijing)
Beijing Institute of Lifeomics · Research Unit of Proteomics Driven Cancer Precision Medicine, Chinese Academy of Medical Sciences
Beijing Institute of Lifeomics · State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing)
International Academy of Phronesis Medicine (Guangdong)
National Center for Protein Sciences (Beijing) · State Key Laboratory of Medical Proteomics
State Key Laboratory of Medical Proteomics

Elsewhere

ORCID 0000-0002-0361-2438

Papers

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))
π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025, Nature Communications)
Transforming de novo peptide sequencing by explainable AI (2024, Research Square)
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)
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)

Methods and tools

AI proteomics perspective, ContraNovo, FDR control for AI-based de novo sequencing, Living proteomics benchmark, π-HelixNovo, π-HelixNovo2, π-MSNet, π-PrimeNovo, π-xNovo

Co-authors (135)

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.

Tianze Ling strength 0.89, 10 shared
Yu Wang strength 0.58, 7 shared
Tingpeng Yang strength 0.56, 6 shared
Yonghong He strength 0.54, 5 shared
Zhendong Liang strength 0.54, 5 shared
Linhai Xie strength 0.53, 5 shared
Boyan Sun strength 0.41, 5 shared
Leyuan Li strength 0.41, 5 shared
Fuchu He strength 0.38, 5 shared
Siqi Sun strength 0.29, 5 shared
Xiang Zhang (Shanghai AI Lab) strength 0.28, 4 shared
Zhi Jin strength 0.28, 4 shared
Nanqing Dong strength 0.26, 3 shared
Sheng Xu strength 0.26, 3 shared
Wanli Ouyang strength 0.26, 3 shared
Zhiqiang Gao strength 0.26, 3 shared
Fan Xu strength 0.18, 2 shared
Xiansong Huang strength 0.18, 2 shared
Chengxin Dai strength 0.18, 2 shared
Guangshuai Wang strength 0.13, 2 shared
Guibin Wang strength 0.13, 2 shared
Laks V.S. Lakshmanan strength 0.13, 2 shared
Muhammad Abdul-Mageed strength 0.13, 2 shared
Zijie Qiu strength 0.13, 2 shared
Yeye Leng strength 0.10, 1 shared
Yun Yang strength 0.10, 1 shared
Yasset Perez-Riverol strength 0.09, 2 shared
Yunping Zhu strength 0.09, 2 shared
Cheng Lai strength 0.09, 1 shared
Jiangli Hu strength 0.09, 1 shared
Yue Yu strength 0.09, 1 shared
Zexuan Yi strength 0.09, 1 shared
Huali Xu strength 0.08, 1 shared
Mingze Bai strength 0.08, 1 shared
Qingyuan Zhang strength 0.08, 1 shared
Timo Sachsenberg strength 0.08, 1 shared
Xiaowei Huang strength 0.08, 1 shared
Yang Qiu strength 0.08, 1 shared
Yi Liu strength 0.08, 1 shared
Haixu Tang strength 0.04, 2 shared

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