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Record W3128211347 · doi:10.1038/s41467-021-21052-3

Author Correction: Characterising the loss-of-function impact of 5’ untranslated region variants in 15,708 individuals

2021· erratum· en· W3128211347 on OpenAlexaff
Nicola Whiffin, Konrad J. Karczewski, Xiaolei Zhang, Sonia Chothani, Miriam J. Smith, D. Gareth Evans, Angharad M. Roberts, Nicholas M. Quaife, Sebastian Schäfer, Owen J. L. Rackham, Jessica Alföldi, Anne O’Donnell‐Luria, Laurent C. Francioli, Irina M. Armean, Eric Banks, Louis Bergelson, Kristian Cibulskis, Ryan L. Collins, Kristen M. Connolly, Miguel Covarrubias, Beryl B. Cummings, Stacey Donnelly, Yossi Farjoun, Steven Ferriera, Stacey Gabriel, Laura D. Gauthier, Jeff Gentry, Namrata Gupta, Thibault Jeandet, Diane Kaplan, Kristen M. Laricchia, Christopher Llanwarne, Eric Vallabh Minikel, Ruchi Munshi, Sam Novod, Nikelle Petrillo, Timothy Poterba, David Roazen, Valentín Ruano-Rubio, Andrea Saltzman, Kaitlin E. Samocha, Molly Schleicher, Cotton Seed, Matthew Solomonson, José Soto, Grace Tiao, Kathleen Tibbetts, Charlotte Tolonen, Christopher Vittal, Gordon Wade, Arcturus Wang, Qingbo S. Wang, Nicholas A. Watts, Ben Weisburd, Carlos A. Aguilar‐Salinas, Tariq Ahmad, Christine M. Albert, Diego Ardissino, Gil Atzmon, J. A. Barnard, Laurent Beaugerie, Emelia J. Benjamin, Michael Boehnke, Lori L. Bonnycastle, Erwin P. Böttinger, Donald W. Bowden, Matthew J. Bown, John C. Chambers, Juliana C.N. Chan, Daniel I. Chasman, Judy H. Cho, Mina K. Chung, Bruce M. Cohen, Adolfo Correa, Dana Dabelea, Dawood Darbar, Ravindranath Duggirala, Josée Dupuis, Patrick T. Ellinor, Roberto Elosúa, Jeanette Erdmann, Tõnu Esko, Martti Färkkilâ, José C. Florez, André Franke, Gad Getz, Benjamin Gläser, Stephen J. Glatt, David B. Goldstein, Clicerio González, Leif Groop, Christopher Haiman, Craig L. Hanis, Matthew Harms, Mikko Hiltunen, Matti Holi, Christina M. Hultman, Mikko Kallela, Jaakko Kaprio, Sekar Kathiresan, Bong‐Jo Kim, Young Jin Kim, George Kirov, Jaspal S. Kooner, Seppo Koskinen, Harlan M. Krumholz, Subra Kugathasan, Soo Heon Kwak, Markku Laakso, Terho Lehtimäki, Ruth J. F. Loos, Steven A. Lubitz, Ronald C.W., Jaume Marrugat, Kari M. Mattila, Steven A. McCarroll, Mark I. McCarthy, Dermot McGovern, Ruth McPherson, James B. Meigs, Olle Melander, Andres Metspalu, Peter M. Nilsson, Michael O‘Donovan, Döst Öngür, Lorena Orozco, Michael J. Owen, Aarno Palotie, Kyong Soo Park, Carlos N. Pato, Ann E. Pulver, Nazneen Rahman, Anne M. Remes, John D. Rioux, Samuli Ripatti, Dan M. Roden, Danish Saleheen, Veikko Salomaa, Nilesh J. Samani, Jeremiah M. Scharf, Heribert Schunkert, Moore B. Shoemaker, Pamela Sklar, Hilkka Soininen, Harry Sokol, Tim D. Spector, Patrick F. Sullivan, Jaana Suvisaari, E Shyong Tai, Yik Ying Teo, Ming T. Tsuang, Dan Turner, Teresa Tusié‐Luna, Erkki Vartiainen, Hugh Watkins, Rinse K. Weersma, Maija Wessman, James G. Wilson, Ramnik J. Xavier, Marquis P. Vawter, Stuart A. Cook, Paul J.R. Barton, Daniel G. MacArthur, James S. Ware

Bibliographic record

VenueNature Communications · 2021
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsUniversité de MontréalMontreal Heart InstituteUniversity of Ottawa
FundersBritish Heart Foundation
KeywordsFunction (biology)Computational biologyUntranslated regionBiologyLoss functionGeneticsGeneMessenger RNAPhenotype

Abstract

fetched live from OpenAlex

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21052-3

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.005
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.074
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0750.042

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 head0.018
GPT teacher head0.325
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicMachine Learning in BioinformaticsFrench-language works237,207