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Abstract SY10-02: Pan-cancer study of recurrent and heterogeneous RNA aberrations and association with whole-genome variants

2017· article· en· W2740313962 on OpenAlexaff
Samirkumar B. Amin, Philip Awadalla, Andrew V. Biankin, Paul C. Boutros, Alvis Brāzma, Angela N. Brooks, Claudia Calabrese, David Chang, Aurélien Chateigner, Ken Chen, Zechen Chong, Brian Craft, Chad J. Creighton, Deniz Demircioğlu, Nuno A. Fonseca, Milana Frenkel‐Morgenstern, Gad Getz, Jonathan Göke, Mary J. Goldman, Liliana Greger, Syed Haider, Yao He, Katherine A. Hoadley, Yuan Ji, André Kahles, Ekta Khurana, Jan O. Korbel, Kjong-Van Lehmann, Han Liang, Fenglin Liu, Maximillian G. Marin, Matthew Meyerson, Akinyemi I. Ojesina, B. F. Francis Ouellette, Chandra Sekhar Pedamallu, Marc D. Perry, Gunnar Rätsch, Roland F. Schwarz, Yuichi Shiraishi, Cameron M. Soulette, Oliver Stegle, Patrick Tan, Alfonso Valencia, Linda Xiang, Christina K. Yung, Junjun Zhang, Fan Zhang, Zemin Zhang, Jingchun Zhu

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyGeneticsGeneExomeComputational biologyRNAGenomeExome sequencingTranscriptomeGene expressionPhenotype

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.053
GPT teacher head0.377
Teacher spread0.324 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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