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
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
12 authors.
Abhirupa GhoshDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0000-0002-3980-4749 Evan P BrennerDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0009-0000-7067-8886 Charmie K VangDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0000-0001-5724-0265 Ethan P WolfeDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0009-0005-7544-7354 Emily A BoyerDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0009-0004-1718-3488 Raymond L LesiyonDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0009-0006-7854-6304 Keenan R ManpearlDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0000-0002-9891-3283 Vignesh SridharDepartment of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI, USA.
Joseph T BurkeDepartment of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI, USA.ORCID 0000-0003-0532-3338 Jacob D KrolDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.ORCID 0009-0001-0523-1508 Jill Mr BilodeauxDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.
Janani RaviDepartment of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz, Aurora, CO, USA.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
Since the clinical introduction of antibiotics in the 1940s, antimicrobial resistance (AMR) has become an increasingly dire threat to global public health. Pathogens acquire AMR much faster than we discover new drugs (antibiotics), warranting innovative methods to better understand its molecular underpinnings. Traditional approaches for detecting AMR in novel bacterial strains are time-consuming and labor-intensive. However, advances in sequencing technology offer a plethora of bacterial genome data, and computational approaches like machine learning (ML) provide an optimistic scope for
Indexed as
AMR predictionAntimicrobial resistanceBacterial pathogensComparative pathogenomicsSupervised machine learning
Identifiers
PMID41279520
PMCPMC12633328
What OpenQuestion holds
Textmetadata
LicenceCC BY
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