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Record W4394224772 · doi:10.6084/m9.figshare.12774912

Additional file 10 of The RNA-binding protein SERBP1 functions as a novel oncogenic factor in glioblastoma by bridging cancer metabolism and epigenetic regulation

2020· dataset· en· W4394224772 on OpenAlexaff
Adam Kosti, Patricia Rosa De Araujo, Weiqing Li, Gabriela D. A. Guardia, Jennifer Chiou, Caihong Yi, Debashish Ray, Fabiana Meliso, Yiming Li, Talia Delambre, Suzanne S. Burns, Franziska K. Lorbeer, Fanny Georgi, Markus Flosbach, Sarah Klinnert, Anne Jenseit, Xiufen Lei, Kevin Ha, Hong Zheng, Renu Pandey, Aleksandra Gruslova, Yogesh K. Gupta, Andrew Brenner, Erzsebet Kokovay, Timothy R. Hughes, Quaid Morris, Pedro A. F. Galante, Stefano Tiziani, Luiz O. F. Penalva

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of Toronto
Fundersnot available
KeywordsEpigeneticsGlioblastomaBridging (networking)RNA-binding proteinRNABiologyCancer researchComputational biologyGeneticsGeneComputer science

Abstract

fetched live from OpenAlex

Additional file 10: Table S9. Gene expression correlation analysis. Genes showing positive and negative (anti-correlation) with SERBP1 in brain samples (Kang dataset) according to R2. Sheet 1, genes showing positive correlation with SERBP1 in brain samples; sheet 2, comparison between genes showing positive correlation with SERBP1 in brain and TCGA GBM samples and GO analysis of shared genes; sheet 3, genes negatively correlated with SERBP1 in brain samples; sheet 4, comparison between genes negatively correlated with SERBP1 in brain and TCGA GBM samples and GO analysis of shared genes.

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)0.008
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: Dataset
Teacher disagreement score0.399
Threshold uncertainty score0.857

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.3990.089

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 head0.018
GPT teacher head0.259
Teacher spread0.241 · 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
Published2020
Admission routes1
Has abstractyes

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