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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 19 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.

fundno affunlabeled
Big Data–Led Cancer Research, Application, and Insights
James A. L. Brown, Tríona Ní Chonghaile, Kyle B. Matchett, Niamh Lynam‐Lennon, Patrick A. Kiely
2016· article· en· Cancer Research· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
GrapHi-C: graph-based visualization of Hi-C datasets
Kimberly MacKay, Anthony Kusalik, Christopher H. Eskiw
2018· article· en· BMC Research Notes· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Binary interactome models of inner- versus outer-complexome organisation
L. Lambourne, Anupama Yadav, Yang Wang, Alice Desbuleux, Dae‐Kyum Kim, Tiziana M. Cafarelli +33 more
2021· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Clustering PPI data by combining FA and SHC method
Xiujuan Lei, Ying Chao, Fang‐Xiang Wu, Jin Xu
2015· article· en· BMC Genomics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Introducing WIREs Data Mining and Knowledge Discovery
Witold Pedrycz
2011· article· en· Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Transcriptomic congruence analysis for evaluating model organisms
Wei Zong, Tanbin Rahman, Li Zhu, Xiangrui Zeng, Yingjin Zhang, Jian Zou +8 more
2023· article· en· Proceedings of the National Academy of Sciences· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About