In one paragraphArticle in bioRxiv : the preprint server for biology, 2023. 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, 0 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
12 authors at 8 institutions in 3 countries.
Daniel S HimmelsteinDepartment of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Related Sciences.ORCID 0000-0002-3012-7446 Michael ZietzDepartment of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Department of Biomedical Informatics, Columbia University, New York, New York, United States of America.ORCID 0000-0003-0539-630X Vincent RubinettiDepartment of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America.ORCID 0000-0002-4655-3773 Kyle KlosterCarbon, Inc.; Department of Computer Science, North Carolina State University, Raleigh, North Carolina, United States of America.ORCID 0000-0001-5678-7197 Benjamin J HeilGenomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania.ORCID 0000-0002-2811-1031 Faisal AlquaddoomiDepartment of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America; Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America.ORCID 0000-0003-4297-8747 Dongbo HuDepartment of Pathology, Perelman School of Medicine University of Pennsylvania, Philadelphia PA, USA.ORCID 0000-0001-7870-8242 David N NicholsonDepartment of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine University of Pennsylvania, Philadelphia PA, USA.ORCID 0000-0003-0002-5761 Yun HaoGenomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA.ORCID 0000-0002-1684-0085 Michael W NagleIntegrative Biology, Internal Medicine Research Unit, Worldwide Research, Development, and Medicine, Pfizer Inc, Cambridge, Massachusetts, United States of America; Neurogenomics, Translational Sciences, Neurology Business Group, Eisai Inc, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-4677-7582 Casey S GreeneDepartment of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America; Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America.ORCID 0000-0001-8713-9213 Translational Therapeutics (United States) · USGordon and Betty Moore Foundation · USUniversity of Pennsylvania · USColorado School of Public Health · USNational Institutes of Health · USNorth Carolina State University · USPfizer (United States) · USUniversity of Utah · US
Funding
Training Program in Computational GenomicsT32HG000046 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI JUNHYONG KIM, Mingyao Li · 1999 to 2026
$9.5MNetwork-based algorithms for target identification and drug repositioning from genetic associationsR01HG010067 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI GREENE, CASEY S · 2018 to 2022
$3.2MCharacterization of high-grade serous ovarian cancer subtypes via single-cell profilingR01CA237170 · NCI · UNIVERSITY OF PENNSYLVANIA · PI DOHERTY, JENNIFER A., GREENE, CASEY S · 2019 to 2024
$3.0MNCI NIH HHS R01 CA237170NHGRI NIH HHS R01 HG010067NHGRI NIH HHS T32 HG000046
6 · The paper itselfAbstract
Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the
Identifiers
PMID36711546
PMCPMC9882000
OpenAlexW3086134442
What OpenQuestion holds
Textmetadata
LicenceCC BY
Read underepoch 390