MétaCan
Menu
Back to cohort
Record W2942646054 · doi:10.1021/acs.analchem.9b00658

First Community-Wide, Comparative Cross-Linking Mass Spectrometry Study

2019· article· en· W2942646054 on OpenAlexaff
Claudio Iacobucci, Christine Piotrowski, Ruedi Aebersold, B.C. do Amaral, Philip Andrews, Katja Bernfur, Christoph H. Borchers, Nicolas I. Brodie, James E. Bruce, Yong Cao, Stéphane Chaignepain, Juan D. Chavez, Stéphane Claverol, Jürgen Cox, Trisha N. Davis, Gianluca Degliesposti, Meng‐Qiu Dong, Nufar Edinger, Cecilia Emanuelsson, Marina Gay, Michael Götze, Francisco Gomes‐Neto, Fábio C. Gozzo, Craig Gutierrez, Caroline Haupt, Albert J. R. Heck, Franz Herzog, Lan Huang, Michael R. Hoopmann, Nir Kalisman, Oleg Klykov, Zdeněk Kukačka, Fan Liu, Michael J. MacCoss, Karl Mechtler, Ravit Mesika, Robert L. Moritz, Nagarjuna Nagaraj, Victor Nesati, Ana Gisele C. Neves‐Ferreira, Robert Ninnis, Petr Novák, Francis J. O’Reilly, Matthias Pelzing, Evgeniy V. Petrotchenko, Lolita Piersimoni, Manolo Plasencia, Tara L. Pukala, Kasper D. Rand, Juri Rappsilber, Dana Reichmann, Carolin Sailer, Chris P. Sarnowski, Richard A. Scheltema, Carla Schmidt, David C. Schriemer, Yi Shi, Mark Skehel, Moriya Slavin, Frank Sobott, Victor Solis‐Mezarino, Heike Stephanowitz, Florian Stengel, Christian E. Stieger, Esben Trabjerg, Michael J. Trnka, Marta Vilaseca, Rosa Viner, Yufei Xiang, Şule Yılmaz, Alex Zelter, Daniel S. Ziemianowicz, Alexander Leitner, Andrea Sinz

Bibliographic record

VenueAnalytical Chemistry · 2019
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsMcGill UniversityGenome British ColumbiaJewish General HospitalUniversity of CalgaryUniversity of Victoria
FundersEuropean Cooperation in Science and TechnologyNational Institute of General Medical SciencesWellcome Trust
KeywordsChemistryMass spectrometryEnvironmental chemistryChromatography

Abstract

fetched live from OpenAlex

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0020.006
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.031
GPT teacher head0.328
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations156
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207