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Record W2273401678 · doi:10.1016/j.dib.2016.01.036

Dataset on the effects of CYB5D2 on the distribution of HeLa cervical cancer cell cycle

2016· article· en· W2273401678 on OpenAlexafffund
Yanyun Xie, Yen Ting Shen, Anil Kapoor, Diane Ojo, Fengxiang Wei, Jason De Melo, Xiaozeng Lin, Nicholas Wong, Judy Yan, Lijian Tao, Pierre Major

Bibliographic record

VenueData in Brief · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaHeart and Stroke Foundation of Canada
KeywordsHeLaCervical cancerCarcinogenesisImmunohistochemistryCell cycleCancerCancer researchMedicineOncologyBiologyCellInternal medicineGenetics

Abstract

fetched live from OpenAlex

We have recently reported that CYB5D2 plays a role in suppression of cervical cancer tumorigenesis, "CYB5D2 displays tumor suppression activities towards cervical cancer" [1]. We provide the accompany data here describing the effects of CYB5D2 overexpression and addition of recombinant CYB5D2 on HeLa cell cycle distribution. Furthermore, we will present the conditions used to specifically determine CYB5D2 expression in primary cervical and cervical cancer tissues using immunohistochemistry (IHC) and the patient cohort involved in assessing the CYB5D2 protein levels in primary cervical and cervical cancer tissues.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.009

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 head0.011
GPT teacher head0.280
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 designBench or experimental
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

Citations6
Published2016
Admission routes2
Has abstractyes

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