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Raw data for Figures 2-6 in ‘An assay to measure poly(ADP ribose) glycohydrolase (PARG) activity in cells’

2016· dataset· en· W4416598353 on OpenAlexaff
Dominic I. James, Stephen T. Durant, Kay Eckersley, Emma Fairweather, Louise A. Griffiths, Nicola Hamilton, Paul Kelly, Mark J. O’Connor, Kerry Shea, Ian D. Waddell, Donald Ogilvie

Bibliographic record

VenueFaculty of 1000 Research Ltd · 2016
Typedataset
Languageen
Field
Topic
Canadian institutionsAstraZeneca (Canada)
FundersCancer Research UK
KeywordsMeasure (data warehouse)Raw dataPattern recognition (psychology)Product (mathematics)

Abstract

fetched live from OpenAlex

After a DNA damage signal multiple polymers of ADP ribose attached to poly(ADP) ribose (PAR) polymerases (PARPs) are broken down by the enzyme poly(ADP) ribose glycohydrolase (PARG). Inhibition of PARG leads to a failure of DNA repair and small molecule inhibition of PARG has been a goal for many years. To determine whether biochemical inhibitors of PARG are active in cells we have designed an immunofluorescence assay to detect nuclear PAR after DNA damage. This 384-well assay is suitable for medium throughput high-content screening and can detect cell-permeable inhibitors of PARG from nM to µM potency. In addition, the assay has been shown to work in murine cells and in a variety of human cancer cells. Furthermore, the assay is suitable for detecting the DNA damage response induced by treatment with temozolomide and methylmethane sulfonate (MMS). Lastly, the assay has been shown to be robust over a period of several years.

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)0.010
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.104
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1040.121

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 head0.156
GPT teacher head0.433
Teacher spread0.277 · 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".

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Citations0
Published2016
Admission routes1
Has abstractno

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