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Record W3175892313 · doi:10.1038/s41586-021-03648-3

Clonal fitness inferred from time-series modelling of single-cell cancer genomes

2021· article· en· W3175892313 on OpenAlexaff
Sohrab Salehi, Farhia Kabeer, Nicholas Ceglia, Mirela Andronescu, Marc Williams, Kieran R. Campbell, Tehmina Masud, Beixi Wang, Justina Biele, Jazmine Brimhall, David Gee, Hakwoo Lee, Jerome Ting, Allen W. Zhang, Hoa Tran, Ciara H. O’Flanagan, Fatemeh Dorri, Nicole Rusk, Teresa Ruiz de Algara, So Ra Lee, Brian Yu Chieh Cheng, Peter Eirew, Takako Kono, Jenifer Pham, Diljot Grewal, Daniel Lai, Richard A. Moore, Andrew J. Mungall, Marco A. Marra, Gregory J. Hannon, Giorgia Battistoni, Dario Bressan, Ian G. Cannell, Hannah Casbolt, Atefeh Fatemi, Cristina Jauset, Tatjana Kovačević, Claire M. Mulvey, Fiona Nugent, Marta Ribes, Isabella Pearsall, Fatime Qosaj, Kirsty Sawicka, Sophia A. Wild, Elena Williams, Emma Laks, Yangguang Li, Austin Smith, Teresa Ruíz, Andrew Roth, Shankar Balasubramanian, Maximillian Lee, Bernd Bodenmiller, Marcel Burger, Laura Kuett, Sandra Tietscher, Jonas Windhager, Edward S. Boyden, Shahar Alon, Yi Cui, Amauche Emenari, Dan Goodwin, Emmanouil D. Karagiannis, Anubhav Sinha, Asmamaw T. Wassie, Carlos Caldas, Alejandra Bruna, Maurizio Callari, Wendy Greenwood, Giulia Lerda, Yaniv Eyal-Lubling, Oscar M. Rueda, Abigail Shea, Robby Becker, Flaminia Grimaldi, Suvi Harris, Sara Lisa Vogl, Joanna Weselak, Johanna A. Joyce, Spencer S. Watson, Ignacio Vázquez-Garćıa, Simon Tavaré, Khanh N. Dinh, Eyal Fisher, Russell Kunes, N. A. Walton, Mohammad Al Sa’d, Nick Chornay, A. Dariush, E. A. González-Solares, Carlos González‐Fernández, A. Yoldaş, Neil S. Millar, Tristan Whitmarsh, Xiaowei Zhuang, Jean Fan, Hsuan Lee, Leonardo A. Sepúlveda, Chenglong Xia, Pu Zheng, Andrew McPherson, Alexandre Bouchard‐Côté, Samuel Aparício, Sohrab P. Shah

Bibliographic record

VenueNature · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsBC Cancer AgencyCanada's Michael Smith Genome Sciences CentreMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteUniversity of TorontoUniversity of British Columbia
FundersNational Human Genome Research InstituteNational Cancer InstituteMedical Research CouncilCancer Research UK
KeywordsBiologyFitness landscapeSomatic evolution in cancerEpistasisGeneticsGenetic FitnessGenomePopulationCancerEvolutionary biologyComputational biologyGeneMedicine

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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 designSimulation or modeling
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

Citations171
Published2021
Admission routes1
Has abstractno

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