In one paragraphArticle in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
34 authors.
Niklas KempynckVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0002-0104-4844 Nathan R ZemkeCenter for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093.ORCID 0000-0002-6326-5925 Seppe De WinterVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0001-7907-1247 Ruchi LohiaPhysiology Department and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-3496-8197 Fabien WehbeMaisonneuve-Rosemont Hospital Research Centre, University of Montreal, Montreal, Quebec, Canada.ORCID 0000-0001-9359-1855 Bocheng LiSchool of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
Darina AbaffyováVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0002-0636-517X Ethan J ArmandBioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093.ORCID 0000-0002-4516-6317 Julie De ManVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0009-0003-7208-8961 Eren Can EksiVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0002-3122-9858 Nikolai HeckerVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0003-1693-4257 Gert HulselmansVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0003-2205-1899 Vasilis KonstantakosVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0002-0332-7506 David MauduitVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0002-2045-227X Gabriele PartelVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0002-4482-3119 Yoshiaki TanakaMaisonneuve-Rosemont Hospital Research Centre, University of Montreal, Montreal, Quebec, Canada.ORCID 0000-0002-2078-9374 Jesse GillisPhysiology Department and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-0936-9774 Bing RenCenter for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093.ORCID 0000-0002-5435-1127 Stein AertsVIB Center for AI & Computational Biology, VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics, Leuven, Belgium.ORCID 0000-0002-8006-0315 Funding
Open-Access AAV Toolbox for Basal Ganglia Cell Types and CircuitsUF1MH128339 · NIMH · ALLEN INSTITUTE · PI BAKKEN, TRYGVE, DAIGLE, TANYA LYNN · 2021 to 2021
$7.3MCenter for Integrated Multi-modal and Multi-scale Nucleome ResearchUM1HG011585 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DULAC, CATHERINE, LEIN, ED · 2020 to 2024
$6.7MDevelopment of tools for cell-type specific labeling of human and mouse neocortical neuronsRF1MH114126 · NIMH · ALLEN INSTITUTE · PI LEIN, ED, LEVI, BOAZ PIRIE · 2017 to 2017
$4.5MCell type selective viral tools to interrogate and correct non-human primate and human brain circuitryUG3MH120095 · NIMH · ALLEN INSTITUTE · PI KALUME, FRANCK K, LEIN, ED · 2020 to 2022
$3.8MCell class- or type-specific viruses for brain-wide labeling and neural circuit examinationRF1MH121274 · NIMH · ALLEN INSTITUTE · PI TASIC, BOSILJKA · 2019 to 2019
$2.6MRevealing the transcriptomic basis of neuronal identity through functional meta-analysisR01MH113005 · NIMH · COLD SPRING HARBOR LABORATORY · PI GILLIS, JESSE · 2017 to 2021
$2.4MNHGRI NIH HHS UM1 HG011585NIMH NIH HHS R01 MH113005NIMH NIH HHS RF1 MH114126NIMH NIH HHS RF1 MH121274NIMH NIH HHS UF1 MH128339NIMH NIH HHS UG3 MH120095
6 · The paper itselfAbstract
Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the 'Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics' to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on
Indexed as
AAVATAC-seqbenchmarkcell typeschallengecortexDNA methylationenhancerHiCmachine learningmousemultiomeprimatesRNA-Seq
Identifiers
PMID39229027
PMCPMC11370467
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC-ND
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