MétaCan
Menu
Cohort builder

4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Cancer Genomics and Diagnostics
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

2,811 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,811 works in the cohort · of 4,299,418page 28 of 57

Labels cover 10 of 2,811 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 2,811 of 2,811 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Network Approaches for Precision Oncology
Shraddha Pai
2022· article· en· Advances in experimental medicine and biology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affvenueunlabeled
Diagnostic and Prognostic DNA-Karyometry for Cancer Diagnostics
Alfred Böcking, David Friedrich, Branko Palcic, Dietrich Meyer-Ebrech, Chen Jin
2021· article· en· Journal of cancer research updates· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Clonal hematopoiesis in metastatic urothelial and renal cell carcinoma
Aslı D. Munzur, Jack V. W. Bacon, Francine Fishbein, Cameron Herberts, Gráinne Donnellan, Cecily Q. Bernales +13 more
2025· article· en· npj Precision Oncology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affaboutunlabeled
750 TWT-101: a first-in-clinic, phase 1/2 study of CFI-402411, a hematopoietic progenitor kinase-1 (HPK1) inhibitor, as a single agent and in combination with pembrolizumab in subjects with advanced solid malignancies
Kyriakos Papadopoulos, Siqing Fu, Erika Hamilton, Alexander I. Spira, Scott A. Laurie, Judy Wang +10 more
2022· article· en· Regular and Young Investigator Award Abstracts· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
In Reply to Zoto Mustafayev and Ozyar
Quynh‐Thu Le, Sue S. Yom, Raymond H. W. Ng, Scott V. Bratman, John J. Welch, K.C. Chan +1 more
2017· letter· en· International Journal of Radiation Oncology*Biology*Physics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Bioinformatics for Cancer Genomics
Yvonne Y. Li, Steven J.M. Jones
2013· book-chapter· en· Elsevier eBooks· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Preexisting Cancer in Transplant Candidates
Greg Knoll, Steven J. Chadban
2018· letter· en· Transplantation· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affaboutunlabeled
Expanding the Galaxy’s reference data
VIJAY NAGAMPALLI, Jayadev Joshi, Nate Coraor, Jennifer Hillman‐Jackson, Dave Bouvier, Marius van den Beek +21 more
2020· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About