Siqi Sun

15 papers in the catalog · China, Shanghai

Siqi Sun: 15 papers in the catalog · China, Shanghai · Fudan University, Shanghai Artificial Intelligence Laboratory · Works on AI proteomics perspective, ContraNovo, CrossNovo

Affiliations

• Fudan University · Research Institute of Intelligent Complex Systems
• Shanghai Artificial Intelligence Laboratory

Elsewhere

ORCID 0000-0001-7240-8724 · OpenAlex

Papers

• AI proteomics: from protein identification to virtual cells (2026, Nature Methods)
• 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

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

Co-authors (130)

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.

• Xiang Zhang (Shanghai AI Lab) 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 0.29, 5 shared
• Tianze Ling strength 0.27, 4 shared
• Wanli Ouyang strength 0.25, 3 shared
• Tiannan Guo strength 0.19, 4 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 0.10, 2 shared
• Yamin Deng strength 0.10, 2 shared
• Yi Chen strength 0.10, 2 shared
• Pu Liu strength 0.08, 1 shared
• Te Zhang strength 0.08, 1 shared
• Xiaofan Zhang strength 0.08, 1 shared
• Zongxiang Nie strength 0.08, 1 shared
• Liujia Qian strength 0.08, 2 shared
• Fuchu He strength 0.08, 2 shared
• Bowen Zhou strength 0.07, 1 shared
• Chenyu You strength 0.07, 1 shared
• Lei Bai 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
• Haixu Tang strength 0.04, 2 shared
• Ming Li strength 0.04, 2 shared
• Wout Bittremieux strength 0.04, 2 shared
• Yingying Sun strength 0.04, 2 shared
• Yu Wang strength 0.04, 2 shared

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