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UbiHub: a data hub for the explorers of ubiquitination pathways

2018· article· en· W2907957654 on OpenAlexafffund
Lihua Liu, David Damerell, Leonidas Koukouflis, Yufeng Tong, Brian D. Marsden, Matthieu Schapira

Bibliographic record

VenueBioinformatics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of WindsorStructural Genomics ConsortiumUniversity of Toronto
FundersEshelman Institute for Innovation, University of North Carolina at Chapel HillJanssen BiotechInnovative Medicines InitiativeNovartis PharmaOntario Genomics InstituteCanada Foundation for InnovationWellcome TrustOntario Ministry of Research, Innovation and ScienceOntario GenomicsFundação de Amparo à Pesquisa do Estado de São PauloGenome CanadaAbbVieEuropean Federation of Pharmaceutical Industries and AssociationsStructural Genomics ConsortiumMerck KGaAPfizerBoehringer Ingelheim
KeywordsUbiquitinProteasomeDrug discoveryDeubiquitinating enzymeComputational biologyProtein degradationBiologyUbiquitin-Protein LigasesUbiquitin ligaseCell biologyBioinformaticsBiochemistry

Abstract

fetched live from OpenAlex

MOTIVATION: Protein ubiquitination plays a central role in important cellular machineries such as protein degradation or chromatin-mediated signaling. With the recent discovery of the first potent ubiquitin-specific protease inhibitors, and the maturation of proteolysis targeting chimeras as promising chemical tools to exploit the ubiquitin-proteasome system, protein target classes associated with ubiquitination pathways are becoming the focus of intense drug-discovery efforts. RESULTS: We have developed UbiHub, an online resource that can be used to visualize a diverse array of biological, structural and chemical data on phylogenetic trees of human protein families involved in ubiquitination signaling, including E3 ligases and deubiquitinases. This interface can inform target prioritization and drug design, and serves as a navigation tool for medicinal chemists, structural and cell biologists exploring ubiquitination pathways. AVAILABILITY AND IMPLEMENTATION: https://ubihub.thesgc.org.

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)0.003
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.050
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.006
Science and technology studies0.0020.001
Scholarly communication0.0050.005
Open science0.0040.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0500.040

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 head0.075
GPT teacher head0.294
Teacher spread0.219 · 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

Citations68
Published2018
Admission routes2
Has abstractyes

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