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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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Single-cell and spatial transcriptomics
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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.

2,127 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.
2,127 works in the cohort · of 4,299,418page 1 of 43

Labels cover 2 of 2,127 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 2,127 of 2,127 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.

afffundunlabeled
Cells of the adult human heart
Monika Litviňuková, Carlos Talavera‐López, Henrike Maatz, Daniel Reichart, Catherine L. Worth, Eric L. Lindberg +27 more
2020· article· en· Nature· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1,771
citations
afffundunlabeled
Eleven grand challenges in single-cell data science
David Lähnemann, Johannes Köster, Ewa Szczurek, Davis J. McCarthy, Stephanie C. Hicks, Mark D. Robinson +45 more
2020· review· en· Genome biology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1,429
citations
afffundunlabeled
Confronting false discoveries in single-cell differential expression
Jordan W. Squair, Matthieu Gautier, Claudia Kathe, Mark A. Anderson, Nicholas D. James, Thomas H. Hutson +8 more
2021· article· en· Nature Communications· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearchconsensus · none
987
citations
fundno affunlabeled
Normalization of mass cytometry data with bead standards
Rachel Finck, Erin F. Simonds, Astraea Jager, Smita Krishnaswamy, Karen Sachs, Wendy J. Fantl +3 more
2013· article· en· Cytometry Part A· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
803
citations
afffundunlabeled
An integrated cell atlas of the lung in health and disease
Lisa Sikkema, Ciro Ramírez-Suástegui, Daniel Strobl, Tessa E. Gillett, Luke Zappia, Elo Madissoon +91 more
2023· article· en· Nature Medicine· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
760
citations
affno abstractunlabeled
The Human Cell Atlas: from vision to reality
Orit Rozenblatt–Rosen, Michael J. T. Stubbington, Aviv Regev, Sarah A. Teichmann
2017· article· en· Nature· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
715
citations
affno abstractunlabeled
Ageing hallmarks exhibit organ-specific temporal signatures
Nicholas Schaum, Benoit Lehallier, Oliver Hãhn, Róbert Pálovics, Shayan Hosseinzadeh, Song Eun Lee +133 more
2020· article· en· Nature· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
694
citations
afffundunlabeled
flowCore: a Bioconductor package for high throughput flow cytometry
Florian Hahne, Nolwenn Le Meur, Ryan R. Brinkman, Byron Ellis, Perry Haaland, Deepayan Sarkar +3 more
2009· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
651
citations
afffundunlabeled
MIFlowCyt: The minimum information about a flow cytometry experiment
Jamie A. Lee, Josef Špidlen, Keith Boyce, Jennifer Cai, Nicholas D. Crosbie, Mark E. Dalphin +28 more
2008· article· en· Cytometry Part A· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearchconsensus · none
514
citations
afffundunlabeled
High-throughput microfluidic single-cell RT-qPCR
Adam K. White, Michael VanInsberghe, Oleh I. Petriv, Mani Hamidi, Darek Sikorski, Marco A. Marra +3 more
2011· article· en· Proceedings of the National Academy of Sciences· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
441
citations
afffundno abstractunlabeled
Highly multiparametric analysis by mass cytometry
Olga Ornatsky, Dmitry Bandura, Vladimir Baranov, Mark Nitz, Mitchell A. Winnik, Scott D. Tanner
2010· review· en· Journal of Immunological Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
369
citations
afffundno abstractunlabeled
Exploring single-cell data with deep multitasking neural networks
Matthew Amodio, David van Dijk, Krishnan Srinivasan, William S. Chen, Hussein Mohsen, Kevin R. Moon +10 more
2019· article· en· Nature Methods· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
350
citations

How this was built: Screen · Findings · About