In one paragraphArticle in bioRxiv : the preprint server for biology, 2026. 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors.
Abhirupa GhoshDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0000-0002-3980-4749 Evan P BrennerDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0009-0000-7067-8886 Emily A BoyerDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0009-0004-1718-3488 Alexander P McKimDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0000-0002-7802-7591 Charmie K VangDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0000-0001-5724-0265 Ethan P WolfeDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0009-0005-7544-7354 David MayerDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.
Raymond L LesiyonDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0009-0006-7854-6304 Janani RaviDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO 80045.ORCID 0000-0001-7443-925X Funding
Computational Bioscience Program Training GrantT15LM009451 · NLM · UNIVERSITY OF COLORADO DENVER · PI Katherina Kechris-Mays, Arjun Krishnan · 2007 to 2026
$11.7MLeveraging evolutionary analyses and machine learning to discover multiscale molecular features associated with antibiotic resistanceU01AI176414 · NIAID · UNIVERSITY OF COLORADO DENVER · PI RAVI, JANANI · 2023 to 2025
$1.4MNIAID NIH HHS U01 AI176414NLM NIH HHS T15 LM009451
6 · The paper itselfAbstract
Motivation: Identifying bacterial antimicrobial resistance (AMR) is critical for diagnostics and treatment, but resistance is a complex trait arising from myriad mechanisms spanning multiple molecular scales. Existing computational approaches often function as black boxes and rarely explore cross-species or multi-drug patterns. We developed Results: The
Indexed as
antimicrobial resistancebacterial genomicsdrug resistance predictioninterpretable modelsmachine learningmultiscale featurespangenomicsR package
Identifiers
PMID42523430
PMCPMC13405290
What OpenQuestion holds
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