MétaCan
Menu
← Back to cohort
Record W4311566589 · doi:10.5281/zenodo.7439018

SDC4: A Target Enabling Package

2022· article· en· W4311566589 on OpenAlexaff
Katerina Gospodinova, V.L. Katis, Paul Brennan, The Emory‐Sage‐SGC TREAT‐AD Center

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Syndecan-4 (SDC4) is a member of a family of syndecan transmembrane receptors, which interact with a variety of extracellular targets. Syndecan signalling leads to endocytosis, endosomal sorting, and exosome export of bound ligands, a process which is dependent on the binding of syndecan-binding protein (SDCBP) to the intracellular tails of syndecans. Cellular trafficking of Aβ is dependent on syndecan/SDCBP activity (2). SDC4 was found within a TMT proteomics network module (Module 42) that contains mostly novel but also well-known AD targets, whose levels are elevated in AD patient brains (1). Inhibition of SDC4 signalling is predicted to reduce the Aβ burden in AD patient brains (3). The aim of this TEP is to generate reagents useful for investigating the role of SDC4 in Alzheimer’s disease, such as a list of validated antibodies, generic knockout/knockdown cell lines and purified protein. Two therapeutic strategies for inhibition of SDC4 will be investigated in this TEP: 1. inhibition of SDC4 activity with an antibody and 2. inhibition of interaction of SDC4 C-terminal tail with SDCBP with small molecules.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.312
Threshold uncertainty score0.981

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.3120.224

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 head0.043
GPT teacher head0.289
Teacher spread0.246 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicCancer Treatment and Pharmacology→French-language works237,207→