Article in Science advances, 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.
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
13 authors.
Daniel KrentzelInstitut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015 Paris, France.ORCID 0000-0002-6234-7259
Julienne PetitInstitut Pasteur, Université Paris Cité, CNRS UMR 3528, Bacterial Cell Cycle Mechanisms Unit, F-75015 Paris, France.ORCID 0000-0002-2312-4044
Yves-Marie BoudehenInstitut Pasteur, Université Paris Cité, CNRS UMR 3528, Bacterial Cell Cycle Mechanisms Unit, F-75015 Paris, France.ORCID 0000-0003-0021-276X
Nassim MahtalInstitut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015 Paris, France.ORCID 0000-0002-1385-4174
Elodie SadowskiAP-HP, Hôpital Pitié-Salpêtrière, Service de bactériologie, Centre National de Référence des mycobactéries et de la résistance des mycobactéries aux antituberculeux, F-75013 Paris, France.
Agnès ZettorInstitut Pasteur, Université Paris Cité, CNRS UMR 3523, Chemogenomic and Biological Screening Core Facility, C2RT, F-75015 Paris, France.ORCID 0000-0002-0592-1963
Alexandra AubryAP-HP, Hôpital Pitié-Salpêtrière, Service de bactériologie, Centre National de Référence des mycobactéries et de la résistance des mycobactéries aux antituberculeux, F-75013 Paris, France.ORCID 0000-0003-4230-4793
Jeanne ChiaravalliInstitut Pasteur, Université Paris Cité, CNRS UMR 3523, Chemogenomic and Biological Screening Core Facility, C2RT, F-75015 Paris, France.ORCID 0000-0001-9135-4565
Nathalie AulnerInstitut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015 Paris, France.ORCID 0000-0002-5800-1853
Stéphanie PetrellaInstitut Pasteur, Université Paris Cité, CNRS UMR 3528, Bacterial Cell Cycle Mechanisms Unit, F-75015 Paris, France.
Pedro M AlzariInstitut Pasteur, Université Paris Cité, CNRS UMR 3528, Structural Microbiology Unit, F-75015 Paris, France.ORCID 0000-0002-4233-1903
Christophe ZimmerInstitut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015 Paris, France.ORCID 0000-0001-9910-1589
Anne Marie WehenkelInstitut Pasteur, Université Paris Cité, CNRS UMR 3528, Bacterial Cell Cycle Mechanisms Unit, F-75015 Paris, France.ORCID 0000-0003-2327-6512
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
To address drug-resistant tuberculosis, the leading single-pathogen infectious killer, drugs with novel modes of action (MoAs) are urgently needed. Phenotypic screening of chemical libraries can identify antimicrobial compounds, but standard screens cannot reveal the MoA of hits, limiting targeted selection of compounds with novel MoAs. Here, we develop a deep learning (DL) model to screen drug-treated
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.
Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed · full record | OpenQuestion