Xiang Zhang (Shanghai AI Lab)

14 papers in the catalog · China, Shanghai, Canada, Vancouver

Xiang Zhang (Shanghai AI Lab): 14 papers in the catalog · China, Shanghai, Canada, Vancouver · Fudan University, Shanghai Artificial Intelligence Laboratory · Works on ContraNovo, CrossNovo, Living proteomics benchmark

Affiliations

Fudan University · Research Institute of Intelligent Complex Systems
Shanghai Artificial Intelligence Laboratory
University of British Columbia
University of British Columbia · Department of Computer Science

Elsewhere

ORCID 0009-0008-1928-5807 · Google Scholar

Papers

A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
Accurate de novo sequencing of the modified proteome with OmniNovo (2025, arXiv)
Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025, NeurIPS 2025)
Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025, arXiv)
Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025, ICML 2025)
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025, ICML 2025)
MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing (2025, bioRxiv)
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025, arXiv)
Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025, arXiv)
Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing (2025, OpenReview)
π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025, Nature Communications)
RankNovo: A Universal Reranking Approach for Robust De Novo Peptide Sequencing (2024, OpenReview)
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)

Methods and tools

ContraNovo, CrossNovo, Living proteomics benchmark, OmniNovo, RankNovo, RefineNovo, XuanjiNovo, π-PrimeNovo

Co-authors (82)

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.

Siqi Sun strength 1.56, 14 shared
Zhiqiang Gao strength 1.54, 13 shared
Nanqing Dong strength 1.47, 12 shared
Sheng Xu strength 1.45, 12 shared
Jiaqi Wei strength 1.35, 11 shared
Zijie Qiu strength 1.33, 11 shared
Zhi Jin strength 1.07, 10 shared
Kai Zou strength 0.38, 3 shared
Cheng Chang strength 0.27, 4 shared
Tianze Ling strength 0.27, 4 shared
Wanli Ouyang strength 0.25, 3 shared
Tiannan Guo strength 0.17, 3 shared
Boyan Sun strength 0.13, 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
Leyuan Li strength 0.13, 2 shared
Muhammad Abdul-Mageed strength 0.13, 2 shared
Jun A strength 0.08, 1 shared
Pu Liu strength 0.08, 1 shared
Te Zhang strength 0.08, 1 shared
Xiaofan Zhang strength 0.08, 1 shared
Yamin Deng strength 0.08, 1 shared
Yi Chen strength 0.08, 1 shared
Zongxiang Nie strength 0.08, 1 shared
Bowen Zhou strength 0.07, 1 shared
Chenyu You strength 0.07, 1 shared
Lei Bai strength 0.07, 1 shared
Liujia Qian strength 0.07, 1 shared
Ning Ding strength 0.07, 1 shared
Shang Qu strength 0.07, 1 shared
Xinjie Mao strength 0.07, 1 shared
Yuejin Yang strength 0.07, 1 shared
Yuhan Chen strength 0.07, 1 shared
Fuchu He strength 0.06, 1 shared
Alfred Nilsson strength 0.02, 1 shared
Baozhen Shan strength 0.02, 1 shared
Benjamin A. Neely strength 0.02, 1 shared
Bin Ma strength 0.02, 1 shared
Ceder Dens strength 0.02, 1 shared

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