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Record W4391526988 · doi:10.1016/j.cstres.2023.12.002

J-domain proteins: From molecular mechanisms to diseases

2024· article· en· W4391526988 on OpenAlexaff
Jarosław Marszałek, Paolo De Los Rios, Douglas Cyr, Matthias P. Mayer, Vasista Adupa, Claes Andréasson, Gregory L. Blatch, Janice E.A. Braun, Jeffrey L. Brodsky, Bernd Bukau, J. Paul Chapple, Charlotte Conz, Sébastien Dementin, Pierre Genevaux, Olivier Genest, Pierre Goloubinoff, Jason E. Gestwicki, Colin M. Hammond, Justin K. Hines, Koji Ishikawa, Łukasz A. Joachimiak, Janine Kirstein, Krzysztof Liberek, Dejana Mokranjac, Nadinath B. Nillegoda, Carlos H.I. Ramos, Mathieu E. Rebeaud, David Ron, Sabine Rospert, Chandan Sahi, Reut Shalgi, Bartłomiej Tomiczek, Ryo Ushioda, Elizaveta Ustyantseva, Yihong Ye, Maciej Żylicz, Harm H. Kampinga

Bibliographic record

VenueCell Stress and Chaperones · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of Calgary
FundersBiotechnology and Biological Sciences Research CouncilNational Institute of General Medical SciencesAtaxia UK
KeywordsDomain (mathematical analysis)Function (biology)Computational biologyBiologyComputer scienceEvolutionary biology

Abstract

fetched live from OpenAlex

J-domain proteins (JDPs) are the largest family of chaperones in most organisms, but much of how they function within the network of other chaperones and protein quality control machineries is still an enigma. Here, we report on the latest findings related to JDP functions presented at a dedicated JDP workshop in Gdansk, Poland. The report does not include all (details) of what was shared and discussed at the meeting, because some of these original data have not yet been accepted for publication elsewhere or represented still preliminary observations at the time.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.244
Teacher spread0.238 · 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 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

Citations19
Published2024
Admission routes1
Has abstractyes

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