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Record W7119539644 · doi:10.5281/zenodo.3408355

Summary statistics for the InsPIRE consortium (pancreatic islets eQTLs)

2019· dataset· W7119539644 on OpenAlexafffund
Ana Viñuela, Arushi Varshney, Rashmi B. Prasad, Olof Asplund, Amanda Bennett, Michael Boehnke, Andrew Brown, Michael Erdos, João Fadista, Ola Hansson, Gad Hatem, Cédric Howald, Apoorva K. Iyengar, Paul Johnson, Ulrika Krus, Patrick E. MacDonald, Anubha Mahajan, Jocelyn E. Manning Fox, Narisu Narisu, Vibe Nylander, Peter Orchard, NIkolaos I. Panousis, A. J. Payne, Michael Stitzel, Swarooparani Vadlamudi, Ryan Welch, Francis S. Collins, Karen Mohlke, Anna Gloyn, Emmanouil Dermitzakis, Leif Groop, Stephen Parker

Bibliographic record

VenueDiscovery Research Portal (University of Dundee) · 2019
Typedataset
Language
Field
Topic
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesMedical Research CouncilAlberta Diabetes FoundationVetenskapsrådetNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean CommissionNovo NordiskNational Institute for Health and Care ResearchNational Human Genome Research InstituteWellcome TrustUniversity of OxfordStiftelsen för Strategisk ForskningEuropean Foundation for the Study of DiabetesAstraZenecaCanadian Institutes of Health ResearchAmerican Diabetes Association
KeywordsPancreatic isletsSummary statisticsIsletGene expressionExpression (computer science)Gene

Abstract

fetched live from OpenAlex

Summary statistics for the InsPIRE study: Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes This datasets includes eQTLs from 420 pancreatic islets (exon and gene level quantifications) and 27 beta-cells FAC sorted (exon quantifications) form RNA-Seq samples. Full summary statistics and independent associations are included. The full description of methods is currently available here: Bioxiv (https://www.biorxiv.org/content/10.1101/655670v1).

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)0.004
metaresearch head score (Gemma)0.029
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.113
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1130.028

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 head0.087
GPT teacher head0.345
Teacher spread0.258 · 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
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
Published2019
Admission routes2
Has abstractyes

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