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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.

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Bioinformatics and Genomic Networks
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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.

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

1,922 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,922 works in the cohort · of 4,299,418page 16 of 39

Labels cover 6 of 1,922 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 1,922 of 1,922 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.

affunlabeled
Clique Cover on Sparse Networks
Mathieu Blanchette, Ethan Kim, Adrian Vetta
2012· book-chapter· en· Society for Industrial and Applied Mathematics eBooks· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
15
citations
afffundno abstractunlabeled
Network science in clinical trials: A patient-centered approach
Venkata Manem, Roberto Salgado, Philippe Aftimos, Christos Sotiriou, Benjamin Haibe‐Kains
2017· review· en· Seminars in Cancer Biology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
15
citations
fundno affunlabeled
Brain tumour genetic network signatures of survival
James K. Ruffle, Samia Mohinta, Guilherme Pombo, Robert M. Gray, Valeriya Kopanitsa, Faith Lee +3 more
2023· article· en· Brain· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
The indispensable genome
Charles Boone, Brenda Andrews
2015· letter· en· Science· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Bayesian integration of networks without gold standards
Jochen Weile, Katherine James, Jennifer Hallinan, Simon Cockell, Phillip Lord, Anil Wipat +1 more
2012· article· en· Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
14
citations
afffundunlabeled
Efficient strategies for screening large-scale genetic interaction networks
Raamesh Deshpande, Justin Nelson, Scott W. Simpkins, Michael Costanzo, Jeff S. Piotrowski, Sheena C. Li +2 more
2017· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
14
citations

How this was built: Screen · Findings · About