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Record W2965551559 · doi:10.1016/j.ccell.2019.07.003

Therapeutic Targeting of RNA Splicing Catalysis through Inhibition of Protein Arginine Methylation

2019· article· en· W2965551559 on OpenAlexafffund
Jia Yi Fong, Luca Pignata, Pierre-Alexis Goy, Kimihito C. Kawabata, Stanley Chun-Wei Lee, Cheryl M. Koh, Daniele Musiani, Enrico Massignani, Andriana G. Kotini, Alex Penson, Cheng Mun Wun, Yudao Shen, Megan Schwarz, Diana Low, Alexander Rialdi, Michelle Ki, Heike Wollmann, Slim Mzoughi, Florence Gay, Christine Thompson, Timothy K. Hart, Olena Barbash, Genna M. Luciani, Magdalena M. Szewczyk, Bas J. Wouters, Ruud Delwel, Eirini P. Papapetrou, Dalia Baršytė-Lovejoy, C.H. Arrowsmith, Mark D. Minden, Jian Jin, Ari Melnick, Tiziana Bonaldi, Omar Abdel‐Wahab, Ernesto Guccione

Bibliographic record

VenueCancer Cell · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsPrincess Margaret Cancer CentreStructural Genomics ConsortiumOntario Institute for Cancer ResearchUniversity of Toronto
FundersEshelman Institute for Innovation, University of North Carolina at Chapel HillJanssen PharmaceuticalsNational Institute of General Medical SciencesNational Medical Research CouncilInnovative Medicines InitiativeFondazione Umberto VeronesiCancer Science Institute of Singapore, National University of SingaporeNational Research Foundation of KoreaMinistry of Education - SingaporeNational Research Foundation SingaporeMinistero della SaluteCanada Foundation for InnovationMerckOntario Ministry of Economic Development and InnovationCycle for SurvivalPershing Square FoundationNational Cancer InstituteCollege of Natural Resources, University of California BerkeleyNational Heart, Lung, and Blood InstituteEdward P. Evans FoundationAssociazione Italiana per la Ricerca sul CancroGenome CanadaFundação de Amparo à Pesquisa do Estado de São PauloAstraZenecaEuropean Hematology AssociationStarr FoundationNational Institutes of HealthLeukemia and Lymphoma Society of CanadaPfizerAmerican Glaucoma SocietyTakeda Pharmaceuticals U.S.A.Leukemia and Lymphoma SocietyAmgenBoehringer IngelheimNational University of SingaporeNovartis PharmaAbbVieHenry and Marilyn Taub FoundationWellcome TrustAstellas Pharma US
KeywordsMethylationArginineRNA splicingRNAChemistryRNA-binding proteinCancer researchAlternative splicingCell biologyComputational biologyBiochemistryBiologyMessenger RNAAmino acidGene

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.245
Teacher spread0.238 · 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
GenreEmpirical

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

Citations279
Published2019
Admission routes2
Has abstractno

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