Living proteomics benchmark

benchmark

Living proteomics benchmark: benchmark. Community-driven, continuously-updated benchmarking resource for deep-learning de novo peptide sequencing: evaluates tools across a broad span of experimental conditions…

Community-driven, continuously-updated benchmarking resource for deep-learning de novo peptide sequencing: evaluates tools across a broad span of experimental conditions and proteomic applications rather than one frozen test set. Nature Methods Registered Report co-authored by 53 researchers spanning most of the groups that build the tools being benchmarked.

Kind benchmark

Code

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Data

What the papers describing this method trained it on and reported its results on, as each paper states it. A version is named only where the paper names one.

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Paper describing it

Authors (53)

Marina Pominova, Jeroen Van Goey, Charlotte Adams, Ceder Dens, Shaorong Chen, Guillaume Deflandre, William E. Fondrie, Cheng Ge, Zhi Jin, Daniela Klaproth-Andrade, Konstantinos Kalogeropoulos, Joel Lapin, Tianze Ling, Kaiyuan Liu, Qixin Liu, Zhongzhi Luan, Alfred Nilsson, Christian Nix, Yingying Sun, Tim Van Den Bossche, Ruitao Wu, Jun Xia, Qingyang Xiao, Shu Yang, Tingpeng Yang, Chenyu Yao, Melih Yilmaz, Di Zhang, Xiang Zhang (Shanghai AI Lab), Jingbo Zhou, Cheng Chang, Shan Chang, Ekapol Chuangsuwanich, Julien Gagneur, Laurent Gatto, Tiannan Guo, Lukas Käll, Ming Li, Stan Z. Li, Benjamin A. Neely, Michael R. Shortreed, Sira Sriswasdi, Baozhen Shan, Siqi Sun, Haixu Tang, Haipeng Wang, Yu Wang, Mathias Wilhelm, Lei Xin, Bin Ma, William Stafford Noble, Timothy P. Jenkins, Wout Bittremieux

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