Evidence map›Paper›PMID 41589861›Full record

ArticleMicrobiology spectrum2026

Evaluation of a machine learning system for genomic antimicrobial susceptibility determination on a clinically representative test set.

Jason D Wittenbach, Arolyn Conwill, Hayden Sansum, Alison Gassett, Adam Gardner, Allison Brookhart, Talia Hollowell, Paul Knysh, Nicholas B Worley, Nicole Billings and 8 more

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Microbiology spectrum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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 registry

The 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 literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Frontiers in bioinformatics · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Jason D WittenbachDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.ORCID 0000-0003-2937-9758
Arolyn ConwillDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.ORCID 0000-0002-6052-0345
Hayden SansumDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Alison GassettDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Adam GardnerDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Allison BrookhartDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Talia HollowellDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Paul KnyshDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Nicholas B WorleyDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Nicole BillingsDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Ian C HerriottDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Julie A ShimabukuroDepartment of Pathology and Laboratory Medicine, University of California Irvine Health, Orange, California, USA.
Kathleen A QuanEpidemiology and Infection Prevention, University of California Irvine Health, Irvine, California, USA.
Keith M MadeyEpidemiology and Infection Prevention, University of California Irvine Health, Irvine, California, USA.
Susan S HuangEpidemiology and Infection Prevention, University of California Irvine Health, Irvine, California, USA.
Mohamad R A SaterDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.
Cassiana E BittencourtDepartment of Pathology and Laboratory Medicine, University of California Irvine Health, Orange, California, USA.
Miriam H HuntleyDay Zero Diagnostics, Inc, Watertown, Massachusetts, USA.ORCID 0000-0003-2048-7701

Funding

Combating Antibiotic-Resistant Bacteria Biopharmaceutical Accelerator PCA 4500004389
6 · The paper itself

Abstract

Next-generation sequencing is anticipated to transform infectious disease treatment by enabling faster diagnosis, yet a clinical-grade method for genomic AST determination is still lacking. We assessed the accuracy of Keynome IMPORTANCE: Microbial genome sequencing presents an exciting opportunity for rapid diagnosis of infectious diseases, but interpreting the resulting data for clinical use remains a challenge. We report a new machine learning method that predicts a bacterial strain's antibiotic resistance profile based solely on its genomic sequence. This method could lead to new, faster diagnostic tools that quickly identify the most effective antibiotic therapy.

Indexed as

Anti-Bacterial AgentsBacteriaGenomicsMachine LearningBacterial InfectionsDrug Resistance, BacterialGenome, BacterialHigh-Throughput Nucleotide SequencingHumansMicrobial Sensitivity TestsAnti-Bacterial Agentsantimicrobial resistancemachine learningmicrobial genomicsNGS

Identifiers

PMID41589861
PMCPMC12955427

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.