Tianze Ling

13 papers in the catalog · China

Tianze Ling: 13 papers in the catalog · China · Beijing Institute of Lifeomics, Beijing Institute of Lifeomics · Works on AdaNovo, Comprehensive Review of DL-Based De Novo Peptide Sequencing, ContraNovo

Affiliations

Beijing Institute of Lifeomics · National Center for Protein Sciences (Beijing)
Beijing Institute of Lifeomics · State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing)
State Key Laboratory of Medical Proteomics
Tsinghua University
Tsinghua University · School of Life Sciences

Elsewhere

ORCID 0009-0006-6715-2553 · OpenAlex

Papers

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))
A Comprehensive and Systematic Review for Deep Learning-Based De Novo Peptide Sequencing (2025, IJCAI 2025)
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)
NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024, NeurIPS 2024)
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)
AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024, ICML 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

AdaNovo, Comprehensive Review of DL-Based De Novo Peptide Sequencing, ContraNovo, FDR control for AI-based de novo sequencing, Living proteomics benchmark, NovoBench, π-HelixNovo, π-HelixNovo2, π-MSNet, π-PrimeNovo, π-xNovo

Co-authors (89)

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.

Cheng Chang strength 0.89, 10 shared
Jingbo Zhou strength 0.56, 4 shared
Jun Xia strength 0.56, 4 shared
Shaorong Chen strength 0.56, 4 shared
Stan Z. Li strength 0.56, 4 shared
Tingpeng Yang strength 0.56, 6 shared
Yu Wang 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 0.36, 4 shared
Sizhe Liu strength 0.29, 2 shared
Wenjie Du strength 0.29, 2 shared
Siqi Sun strength 0.28, 4 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
Jianwei Yin strength 0.12, 1 shared
Yue Liu strength 0.12, 1 shared
Yeye Leng strength 0.10, 1 shared
Yun Yang strength 0.10, 1 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

Seen in the charts

Back to the full map