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Record W2745658163 · doi:10.1016/j.ccell.2017.07.003

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

2017· article· en· W2745658163 on OpenAlexaff
A. Gordon Robertson, Juliann Shih, Christina Yau, Ewan A. Gibb, Junna Oba, Karen Mungall, Julian M. Hess, Vladislav Uzunangelov, Vonn Walter, Ludmila Danilova, Tara M. Lichtenberg, Melanie H. Kucherlapati, Patrick K. Kimes, Ming Tang, Alexander Penson, Özgün Babur, Rehan Akbani, Christopher A. Bristow, Katherine A. Hoadley, Lisa Iype, Matthew T. Chang, Mohamed H. Abdel‐Rahman, Adrian Ally, J. Todd Auman, Miruna Balasundaram, Saianand Balu, Rameen Beroukhim, İnanç Birol, Tom Bodenheimer, Jay Bowen, Reanne Bowlby, Denise Brooks, Rebecca Carlsen, Colleen M. Cebulla, Andrew D. Cherniack, Lynda Chin, Juok Cho, Eric Chuah, Sudha Chudamani, Carrie Cibulskis, Kristian Cibulskis, Leslie Cope, Timothy Defreitas, John A. Demchok, Laurence Desjardins, Noreen Dhalla, Bita Esmaeli, Ina Felau, Martin L. Ferguson, Scott Frazer, Stacey Gabriel, Julie M. Gastier‐Foster, Nils Gehlenborg, Mark Gerken, Gad Getz, Klaus Griewank, Elizabeth A. Grimm, D. Neil Hayes, Apurva M. Hegde, David I. Heiman, Carmen Helsel, Shital Hobensack, Robert A. Holt, Alan P. Hoyle, Xin Hu, Carolyn M. Hutter, Martine J. Jager, Corbin D. Jones, Steven J.M. Jones, Cyriac Kandoth, Jaegil Kim, Raju Kucherlapati, Eric S. Lander, Michael S. Lawrence, Alexander J. Lazar, Semin Lee, Kristen Leraas, Pei Lin, Jia Liu, Wenbin Liu, Laxmi Lolla, Yiling Lu, Yussanne Ma, Harshad S. Mahadeshwar, Odette Mariani, Marco A. Marra, Michael Mayo, Shaowu Meng, Matthew Meyerson, Piotr A. Mieczkowski, Gordon B. Mills, Richard A. Moore, Lisle E. Mose, Andrew J. Mungall, Bradley A. Murray, Rashi Naresh, Michael S. Noble, Angeliki Pantazi, Michael Parfenov, Peter J. Park, Joel S. Parker, Charles M. Perou, Todd Pihl, Robert Pilarski, Alexei Protopopov, Amie Radenbaugh, Karan Rai, Nilsa C. Ramirez, Xiaojia Ren, Sheila M. Reynolds, Jeffrey Roach, Sergio Roman‐Roman, Jason Roszik, Sara Sadeghi, Gordon Saksena, Xavier Sastre, Dirk Schadendorf, Jacqueline E. Schein, Steven E. Schumacher, Jonathan G. Seidman, Sahil Seth, Geetika Sethi, Margi Sheth, Yan Shi, Carol L. Shields, Ilya Shmulevich, Janae V. Simons, Arun D. Singh, Payal Sipahimalani, Tara Skelly, Heidi J. Sofia, Matthew G. Soloway, Xingzhi Song, Joshua M. Stuart, Qiang Sun, Huandong Sun, Angela Tam, Donghui Tan, Jiabin Tang, Roy Tarnuzzer, Barry S. Taylor, Nina Thiessen, Vésteinn Thórsson, Kane Tse, Umadevi Veluvolu, Roel G.W. Verhaak, Doug Voet, Yunhu Wan, John N. Weinstein, Matthew D. Wilkerson, Lisa Wise, Scott E. Woodman, Tina Wong, Ye Wu, Liming Yang, Lixing Yang, Jean C. Zenklusen, Jiashan Zhang, Hailei Zhang, Erik Zmuda

Bibliographic record

VenueCancer Cell · 2017
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer Agency
FundersNational Eye InstituteCastle BiosciencesNational Human Genome Research InstituteNational Cancer InstituteNational Institutes of HealthBayer
KeywordsMelanomaCancer researchComputational biologyBiologyMedicine

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.033
GPT teacher head0.386
Teacher spread0.353 · 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

Citations919
Published2017
Admission routes1
Has abstractno

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