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
Machine Learning in Bioinformatics
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.

1,034 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.
1,034 works in the cohort · of 4,299,418page 7 of 21

Labels cover 1 of 1,034 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 1,034 of 1,034 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
Neural Networks in Bioinformatics
Masood Zamani, Stefan C. Kremer
2013· book-chapter· en· Intelligent systems reference library· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
fundno affunlabeled
Higher eQTL power reveals signals that boost GWAS colocalization
Jonathan D. Rosen, K. Alaine Broadaway, Sarah M. Brotman, Karen L. Mohlke, Michael I. Love
2025· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Position Weight Matrix and Perceptron
Xuhua Xia
2018· book-chapter· en· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Pairwise Rational Kernels Obtained by Automaton Operations
Abiel Roche-Lima, Michael Domaratzki, Brian Fristensky
2014· book-chapter· en· Lecture notes in computer science· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
2
citations
afffundaboutunlabeled
Extracting a COVID-19 signature from a multi-omic dataset
Baptiste Bauvin, Thibaud Godon, Guillaume Bachelot, Claudia Carpentier, Riikka Huusaari, Maxime Déraspe +3 more
2025· article· en· Frontiers in Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Illuminating Dark Proteins using Reactome Pathways
Lisa Matthews, Guanming Wu, Robin Haw, Timothy Brunson, Nasim Sanati, Solomon I. Shorser +4 more
2022· report· en· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Hidden<scp>M</scp>arkov models and neural networks
Stefan C. Kremer, Pierre Baldi
2005· other· en· Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About