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Record W4318019176 · doi:10.1021/acs.jproteome.2c00620

CaspSites: A Database and Web Application for Experimentally Observed Human Caspase Substrates Using N-Terminomics

2023· article· en· W4318019176 on OpenAlexafffund
Henry Wang, Olivier Julien

Bibliographic record

VenueJournal of Proteome Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCaspaseCleavage (geology)World Wide WebWeb siteComputer scienceDatabaseApoptosisInformation retrievalBiologyThe InternetBiochemistryProgrammed cell death

Abstract

fetched live from OpenAlex

CaspSites is a free-to-use database and web application for experimentally observed human caspase substrates using N-terminomics. It can be accessed and used by all users at the web URL www.caspsites.org. CaspSites stores cleavage site information identified for human caspases 1-9 in lysates and apoptotic cells, collected from their corresponding published studies. The database can be queried, viewed, and exported using the search page of the web application. The main parameters offered are protein substrate, cleavage site (P4-P4') residues, and individual caspase data sets, which can be connected using OR, AND, or NOT logical operators for custom user-built queries. CaspSites will be regularly updated with new experimental findings for understudied caspases, providing researchers insight into the distinctive roles human caspases play in cellular processes by identifying their target proteins in relation to each other.

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)0.002
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.067
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0670.039

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 head0.188
GPT teacher head0.457
Teacher spread0.269 · 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
GenreSoftware

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

Citations9
Published2023
Admission routes2
Has abstractyes

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