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Identification of Susceptibility Loci and Genes for Colorectal Cancer Risk
Chenjie Zeng, Koichi Matsuda, Wei‐Hua Jia, Jiang Chang, Sun‐Seog Kweon, Yong‐Bing Xiang, Aesun Shin, Sun Ha Jee, Dong-Hyun Kim, Ben Zhang, Qiuyin Cai, Xingyi Guo, Jirong Long, Nan Wang, Regina Courtney, Zhizhong Pan, Chen Wu, Atsushi Takahashi, Min‐Ho Shin, Keitaro Matsuo, Fumihiko Matsuda, Yu‐Tang Gao, Jae Hwan Oh, Soriul Kim, Keum Ji Jung, Yoon‐Ok Ahn, Zefang Ren, Honglan Li, Jie Wu, Jiajun Shi, Wanqing Wen, Gong Yang, Bingshan Li, Bu‐Tian Ji, John A. Baron, Sonja I. Berndt, Stéphane Bezieau, Hermann Brenner, Bette J. Caan, Christopher S. Carlson, Andrew T. Chan, Jenny Chang‐Claude, Stephen J. Chanock, David V. Conti, Keith R. Curtis, David Duggan, Charles S. Fuchs, Steven Gallinger, Edward L. Giovannucci, Stephen B. Gruber, Robert W. Haile, Tabitha A. Harrison, Richard B. Hayes, Michael Hoffmeister, John L. Hopper, Li Hsu, Thomas J. Hudson, David J. Hunter, Carolyn M. Hutter, Rebecca D. Jackson, Mark A. Jenkins, Sébastien Küry, Loı̈c Le Marchand, Mathieu Lemire, Noralane M. Lindor, Jing Ma, Polly A. Newcomb, Ulrike Peters, John D. Potter, Conghui Qu, Robert E. Schoen, Daniela Seminara, Martha L. Slattery, Stephen N. Thibodeau, Emily White, Brent W. Zanke, Kendra L. Blalock, Peter T. Campbell, Graham Casey, Christopher K. Edlund, Jane C. Figueiredo, W. James Gauderman, Jian Gong, Roger C. Green, John F. Harju, Eric J. Jacobs, Li Li, Yi Lin, Frank J. Manion, Vı́ctor Moreno, Bhramar Mukherjee, Leon Raskin, Gianluca Severi, Stephanie L. Stenzel, Duncan C. Thomas, Fredrick R. Schumacher, Hyeong Rok Kim, Jin Young Jeong, Ji Won Park, Kazuo Tajima, Sang‐Hee Cho, Michiaki Kubo, Xiao‐Ou Shu, Dongxin Lin, Yi‐Xin Zeng, Wei Zheng
Bibliographic record
VenueGastroenterology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Cancer InstituteNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteNational Key Research and Development Program of ChinaJapan Society for the Promotion of ScienceCanadian Institutes of Health ResearchCanadian Cancer Society Research InstituteSchool of Medicine, Vanderbilt UniversityMike and Josie Harper Cancer Research InstituteNational Institutes of HealthChonnam National University Hwasun HospitalGroupement des Entreprises Françaises dans la lutte contre le CancerNational Institute on AgingFourth Military Medical UniversityVanderbilt-Ingram Cancer CenterChonnam National UniversityMinistry of Education, Culture, Sports, Science and TechnologyNational Research FoundationGénome QuébecMayo ClinicOntario Ministry of Economic Development and InnovationOntario Research FoundationBundesministerium für Bildung und ForschungAssociation Anne de Bretagne GenetiqueConseil Régional des Pays de la LoireUniversity of Southern CaliforniaOntario Institute for Cancer ResearchNational Research Foundation of KoreaVanderbilt UniversityNational Natural Science Foundation of ChinaBroad InstituteMinistero dello Sviluppo Economico
KeywordsBiologyGeneticsLocus (genetics)Genome-wide association studyGeneAlleleSingle-nucleotide polymorphismGenotype
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.398
Threshold uncertainty score0.221
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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 teacher head, 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
Citations132
Published2016
Admission routes1
Has abstractno