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π-HuB: the proteomic navigator of the human body
Fuchu He, Ruedi Aebersold, Mark S. Baker, Xiu‐Wu Bian, Xiaochen Bo, Daniel W. Chan, Cheng Chang, Luonan Chen, Xiangmei Chen, Yu‐Ju Chen, Heping Cheng, Ben C. Collins, Fernando J. Corrales, Jüergen Cox, E Weinan, Jennifer E. Van Eyk, Jia Fan, Pouya Faridi, Daniel Figeys, George F. Gao, Wen Gao, Zu‐Hua Gao, Keisuke Goda, Wilson Wen Bin Goh, Dongfeng Gu, Changjiang Guo, Tiannan Guo, Yuezhong He, Albert J. R. Heck, Henning Hermjakob, Tony Hunter, N. Gopalakrishna Iyer, Ying Jiang, Connie R. Jiménez, Lokesh Joshi, Neil L. Kelleher, Ming Li, Yang Li, Qingsong Lin, Cui Hua Liu, Fan Liu, Guang‐Hui Liu, Yansheng Liu, Zhihua Liu, Teck Yew Low, Ben Lü, Matthias Mann, Anming Meng, Robert L. Moritz, Edouard C. Nice, Guang Ning, Gilbert S. Omenn, Christopher M. Overall, Giuseppe Palmisano, Yaojin Peng, Charles Pineau, Terence Chuen Wai Poon, Anthony W. Purcell, Jie Qiao, Roger R. Reddel, Phillip J. Robinson, Paola Roncada, Chris Sander, Jiahao Sha, Erwei Song, Sanjeeva Srivastava, Aihua Sun, Siu Kwan Sze, Chao Tang, Liujun Tang, Ruijun Tian, Juan Antonio Vizcaíno, Chanjuan Wang, Chen Wang, Xiaowen Wang, Xinxing Wang, Yan Wang, Tobias Weiß, Mathias Wilhelm, Robert Winkler, Bernd Wollscheid, Limsoon Wong, Linhai Xie, Wei Xie, Tao Xu, Tianhao Xu, Liying Yan, Jing Yang, Xiao Yang, John R. Yates, Tao Yun, Qiwei Zhai, Bing Zhang, Hui Zhang, Lingqiang Zhang, Pingwen Zhang, Yukui Zhang, Yu Zi Zheng, Qing Zhong, Yunping Zhu, Chris Soon Heng Tan, Mingxia Gao, Qiang Gao, Yushun Gao, Xuejiang Guo, Jie He, Jun He, Qing‐Yu He, Jinlin Hou, Canhua Huang, Chenxi Jia, Bernhard Küster, Zesong Li, Dong Li, Yan Li, Yanchang Li, Siqi Liu, Xiaonan Liu, Ya Liu, Zhongyang Liu, Haojie Lu, Yongzhan Nie, Mariko Okada, Guojun Qian, Hongqiang Qin, Yu Rao, Zihe Rao, Xianwen Ren, Yan Ren, Feng Shen, Lin Shen, Shicheng Su, Minjia Tan, Ben Zhong Tang, Sheng‐ce Tao, Jian Wang, Tong Wang, Liming Wei, Catherine C. L. Wong, X. Sunney Xie, Xu Li, Ping Xu, Huanming Yang, Jianjun Yang, Mingliang Ye, Wantao Ying, Xiaobo Yu, Ya-xiang Yuan, Qingcun Zeng, Qimin Zhan, Xiao-Fei Zhang, Xu Zhang, Ying Zhang, Nanshan Zhong, Feng Zhou, Yi Zhu, Zemin Zhang
Bibliographic record
VenueNature · 2024
Typereview
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsBrock UniversityUniversity of WaterlooUniversity of British ColumbiaUniversity of Ottawa
FundersNational Institute of Environmental Health SciencesNational Institutes of HealthMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsContext (archaeology)Data scienceHuman proteome projectComputer scienceHuman bodyGovernment (linguistics)Systems biologyHuman healthMultidisciplinary approachComputational biologyProteomicsBiologyArtificial intelligencePolitical scienceMedicine
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.906
Threshold uncertainty score0.999
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.016
GPT teacher head0.366
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
Study designNot applicable
Domainnot available
GenreReview
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations44
Published2024
Admission routes1
Has abstractno