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Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions
Eileen Dareng, Simon G. Coetzee, Jonathan P. Tyrer, Pei-Chen Peng, Will Rosenow, Stephanie Chen, Brian Davis, Felipe Segato Dezem, Ji-Heui Seo, Robbin Nameki, Alberto Luiz P. Reyes, Katja K.H. Aben, Hoda Anton‐Culver, Natalia Antonenkova, Gerasimos Aravantinos, Elisa V. Bandera, Laura E. Beane Freeman, Matthias W. Beckmann, Alicia Beeghly‐Fadiel, Javier Benı́tez, Marcus Q. Bernardini, Line Bjørge, Amanda Black, Natalia Bogdanova, Kelly L. Bolton, James D. Brenton, Agnieszka Budziłowska, Ralf Bützow, Hui Cai, Ian Campbell, Rikki Cannioto, Jenny Chang‐Claude, Stephen J. Chanock, Kexin Chen, Georgia Chenevix‐Trench, Yoke-Eng Chiew, Linda S. Cook, Anna DeFazio, Joe Dennis, Jennifer A. Doherty, Thilo Dörk, Andreas du Bois, Matthias Dürst, Gabrielle Ene, Peter A. Fasching, James M. Flanagan, Renée T. Fortner, Florentia Fostira, Aleksandra Gentry‐Maharaj, Graham G. Giles, Marc T. Goodman, Jacek Gronwald, Christopher A. Haiman, Niclas Håkansson, Florian Heitz, Michelle A.T. Hildebrandt, Estrid Høgdall, Claus Høgdall, Ruea‐Yea Huang, Allan Jensen, Michael E. Jones, Daehee Kang, Beth Y. Karlan, Anthony N. Karnezis, Linda E. Kelemen, Catherine J. Kennedy, Э. К. Хуснутдинова, Lambertus A. Kiemeney, Susanne K. Kjær, Jolanta Kupryjańczyk, Marilyne Labrie, Diether Lambrechts, Melissa C. Larson, Nhu D. Le, Jenny Lester, Lian Li, Jan Lubiński, Michael Lush, Jeffrey R. Marks, Keitaro Matsuo, Taymaa May, Iain A. McNeish, Usha Menon, Stacey A. Missmer, Francesmary Modugno, Melissa Moffitt, Álvaro N.A. Monteiro, Kirsten B. Moysich, Steven A. Narod, Tú Nguyen‐Dumont, Kunle Odunsi, Håkan Olsson, N. Charlotte Onland‐Moret, Sue K. Park, Tanja Pejović, Jennifer B. Permuth, Anna Piskorz, Darya Prokofyeva, Marjorie J. Riggan, Harvey A. Risch, Cristina Rodríguez‐Antona, Mary Anne Rossing, Dale P. Sandler, V. Wendy Setiawan, Kang Shan, Honglin Song, Melissa C. Southey, Helen Steed, Rebecca Sutphen, Anthony J. Swerdlow, Soo‐Hwang Teo, Kathryn L. Terry, Pamela J. Thompson, Liv Cecilie Vestrheim Thomsen, Linda Titus, Britton Trabert, Ruth C. Travis, Shelley S. Tworoger, Ellen Valen, Els Van Nieuwenhuysen, Digna Velez Edwards, Robert A. Vierkant, Penelope M. Webb, Clarice R. Weinberg, R Weise, Nicolas Wentzensen, Emily White, Stacey J. Winham, Alicja Wolk, Yin Ling Woo, Anna H. Wu, Yan Li, Drakoulis Yannoukakos, Nur Zeinomar, Wei Zheng, Argyrios Ziogas, Andrew Berchuck, Ellen L. Goode, David G. Huntsman, Celeste Leigh Pearce, Susan J. Ramus, Thomas A. Sellers, Matthew L. Freedman, Kate Lawrenson, Joellen M. Schildkraut, Dennis J. Hazelett, Jasmine Plummer, Siddhartha Kar, Michelle R. Jones, Paul D.P. Pharoah, Simon A. Gayther
Bibliographic record
VenueThe American Journal of Human Genetics · 2024
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversity of British ColumbiaUniversity of AlbertaPublic Health OntarioPrincess Margaret Cancer CentreUniversity of CalgaryLunenfeld-Tanenbaum Research InstituteBC Cancer AgencyAlberta Health ServicesWomen's College HospitalUniversité de SherbrookeUniversity of TorontoUniversity Health Network
FundersNational Cancer InstituteMedical Research CouncilUniversity of Texas Health Science Center at San AntonioNational Institutes of HealthCedars-Sinai Medical Center
KeywordsOvarian cancerGenome-wide association studySingle-nucleotide polymorphismBiologyEpithelial ovarian cancerOncologyCancerBioinformaticsGeneticsInternal medicineComputational biologyMedicineGenotypeGene
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)0.003
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.018
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 head0.111
GPT teacher head0.416
Teacher spread0.305 · 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
Citations25
Published2024
Admission routes1
Has abstractno