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Record W2564765645 · doi:10.1016/j.ccell.2016.11.003

Integrated (epi)-Genomic Analyses Identify Subgroup-Specific Therapeutic Targets in CNS Rhabdoid Tumors

2016· article· en· W2564765645 on OpenAlexafffund
Jonathon Torchia, Brian Golbourn, Shengrui Feng, King Ching Ho, Patrick Sin‐Chan, Alexandre Vasiljevic, Joseph Norman, Paul Guilhamon, Livia Garzia, Natalia R. Agamez, Mei Lu, Tiffany Sin Yu Chan, Daniel Picard, Pasqualino De Antonellis, Dong-Anh Khuong-Quang, Aline Cristiane Planello, Constanze Zeller, Dalia Baršytė-Lovejoy, Lucie Lafay‐Cousin, Louis Létourneau, Mathieu Bourgey, Man Yu, Deena M.A. Gendoo, Misko Dzamba, Mark Barszczyk, Tiago da Silva Medina, Alexandra N. Riemenschneider, A. Sorana Morrissy, Young‐Shin Ra, Vijay Ramaswamy, Marc Remke, Christopher Dunham, Stephen Yip, Ho‐Keung Ng, Jian‐Qiang Lu, Vivek Mehta, Steffen Albrecht, José Pimentel, Jennifer A. Chan, Gino R. Somers, Cláudia C. Faria, Lúcia Roque, Maryam Fouladi, Lindsey M. Hoffman, Andrew S. Moore, Yin Wang, Seung Ah Choi, Jordan R. Hansford, Daniel Catchpoole, Diane K. Birks, Nicholas K. Foreman, Doug Strother, Álmos Klekner, László Bognár, Miklós Garami, Péter Hauser, Tibor Hortobágyi, Beverly Wilson, Juliette Hukin, Anne‐Sophie Carret, Timothy Van Meter, Eugene Hwang, Amar Gajjar, Shih‐Hwa Chiou, Hideo Nakamura, Helen Toledano, Iris Fried, Daniel W. Fults, Takafumi Wataya, Chris Fryer, David D. Eisenstat, Katrin Scheinemann, Adam Fleming, Donna L. Johnston, Jean Michaud, Shayna Zelcer, Robert Hammond, Samina Afzal, David A. Ramsay, Nongnuch Sirachainan, Suradej Hongeng, Noppadol Larbcharoensub, Richard G. Grundy, Rishi Lulla, Jason Fangusaro, Harriet Druker, Ute Bartels, Ronald Grant, David Malkin, C. Jane McGlade, Theodore Nicolaides, Tarık Tihan, Joanna J. Phillips, Jacek Majewski, Alexandre Montpetit, Guillaume Bourque, Gary D. Bader, Alyssa Reddy, G. Yancey Gillespie, Monika Warmuth‐Metz, Stefan Rutkowski, Uri Tabori, Mathieu Lupien, Michael Brudno, Ulrich Schüller, Torsten Pietsch, Alexander R. Judkins, Cynthia Hawkins, Éric Bouffet, Seung‐Ki Kim, Peter B. Dirks, Michael D. Taylor, Anat Erdreich‐Epstein, C.H. Arrowsmith, Daniel D. De Carvalho, James T. Rutka, Nada Jabado, Annie Huang

Bibliographic record

VenueCancer Cell · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsMcGill Genome CentreMcGill UniversityLondon Health Sciences CentreChildren's Hospital of Western OntarioChildren's Hospital of Eastern OntarioUniversity of OttawaDalhousie UniversityCentre Hospitalier Universitaire Sainte-JustineUniversity Health NetworkUniversity of AlbertaStollery Children's HospitalAlberta Children's HospitalMcGill University Health CentreHospital for Sick ChildrenWestern UniversityMcGill University and Génome Québec Innovation CentreUniversity of TorontoSickKids FoundationChildren's & Women's Health Centre of British ColumbiaUniversity of CalgaryPrincess Margaret Cancer CentreUniversity of British ColumbiaMcMaster UniversityUniversité de Montréal
FundersNational Institute of General Medical SciencesNational Human Genome Research InstituteCanadian Cancer Society Research InstituteGenome CanadaCanadian Institutes of Health ResearchMcGill UniversityGénome QuébecFondation Brain Canada
KeywordsEpigenomicsPDGFRBEpigeneticsSMARCB1DNA methylationBiologyCancer researchBioinformaticsGeneticsGeneChromatin remodelingGene expression

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.313
Teacher spread0.278 · 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

Citations254
Published2016
Admission routes2
Has abstractno

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