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The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies
Claire Redin, Harrison Brand, Ryan L. Collins, Tammy Kammin, Elyse Mitchell, Jennelle C. Hodge, Carrie Hanscom, Vamsee Pillalamarri, Catarina M. Seabra, Mary‐Alice Abbott, Omar Abdul‐Rahman, Erika Aberg, Rhett Adley, Sofía Lizeth Alcaráz‐Estrada, Fowzan S. Alkuraya, Yu An, MaryAnne Anderson, Caroline Antolik, Kwame Anyane‐Yeboa, Joan Atkin, Tina M. Bartell, Jonathan A. Bernstein, Elizabeth Beyer, Ian Blumenthal, Ernie M.H.F. Bongers, Eva H. Brilstra, Chester Brown, Hennie T. Brüggenwirth, Bert Callewaert, Colby Chiang, Ken Corning, Helen Cox, Edwin Cuppen, Benjamin Currall, Tom Cushing, D. David, Matthew A. Deardorff, Annelies Dheedene, Marc D’Hooghe, Bert B.A. de Vries, Dawn Earl, Heather Ferguson, Heather Fisher, David Fitzpatrick, Pamela Gerrol, Daniela Giachino, Joseph Glessner, Troy J. Gliem, Margo Grady, Brett H. Graham, Cristin Griffis, Karen W. Gripp, Andrea Gropman, Andrea Hanson‐Kahn, David J. Harris, Mark A. Hayden, R. Sean Hill, Ron Hochstenbach, Jodi D. Hoffman, Robert J. Hopkin, Monika Weisz Hubshman, A. Micheil Innes, Mira Irons, Melita Irving, Jessie C. Jacobsen, Sandra Janssens, Tamison Jewett, John P. Johnson, Marjolijn C.J. Jongmans, Stephen G. Kahler, David A. Koolen, Jerome Korzelius, Peter M. Kroisel, Yves Lacassie, William Lawless, Emmanuelle Lemyre, Kathleen A. Leppig, Alex V. Levin, Haibo Li, Hong Li, Eric C. Liao, Cynthia Lim, Edward J. Lose, Diane Lucente, Michael J. Macera, Poornima Manavalan, Giorgia Mandrile, Carlo Marcelis, Lauren Margolin, Tamara Mason, Diane Masser‐Frye, Michael McClellan, Cinthya J. Zepeda Mendoza, Björn Menten, Sjors Middelkamp, Liya Regina Mikami, Emily Moe, Shehla Mohammed, Tarja Mononen, Megan Mortenson, Graciela Moya, Aggie Nieuwint, Zehra Ordulu, Sandhya Parkash, Susan P. Pauker, Shahrin Pereira, Danielle Perrin, Katy Phelan, Raul E Piña Aguilar, Pino J. Poddighe, Giulia Pregno, Salmo Raskin, Linda M. Reis, William J. Rhead, Debra Rita, Ivo Renkens, Filip Roelens, Jayla Ruliera, Patrick Rump, Samantha L.P. Schilit, Ranad Shaheen, Rebecca Sparkes, Erica Spiegel, Blair Stevens, Matthew R. Stone, Julia Tagoe, Joseph V. Thakuria, Bregje W.M. van Bon, Jiddeke van de Kamp, Ineke van der Burgt, Ton van Essen, Conny M.A. van Ravenswaaij‐Arts, Markus J. van Roosmalen, Sarah Vergult, Catharina M.L. Volker‐Touw, Dorothy Warburton, Matthew J. Waterman, Susan Wiley, Anna E. Wilson, Maria de la Concepcion A Yerena-de Vega, Roberto T. Zori, Brynn Levy, Han G. Brunner, Nicole de Leeuw, Wigard P. Kloosterman, Erik C. Thorland, Cynthia C. Morton, James F. Gusella, Michael E. Talkowski
Bibliographic record
VenueNature Genetics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsAlberta Health ServicesCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalUniversity of CalgaryDalhousie UniversityIzaak Walton Killam Health Centre
FundersNational Institute of General Medical SciencesNational Institute of Mental HealthPhilippe FoundationFonds Wetenschappelijk OnderzoekGovernment of Jiangsu ProvinceVlaamse regeringMinistry of Education, IndiaNational Institutes of HealthSuzhou Key Medical CenterEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentEuropean CommissionRoyal Society Te ApārangiFundação para a Ciência e a TecnologiaMarch of Dimes FoundationNational Science Foundation
KeywordsBiologyBreakpointGeneticsKaryotypePhenotypeCopy-number variationGenomeChromosomeComputational biologyGeneHuman genomeLocus (genetics)MEF2CMedical geneticsGene expression
Abstract
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 imitationNot 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)0.001
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 score0.003
Threshold uncertainty score0.010
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
Citations354
Published2016
Admission routes1
Has abstractno