Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

Daniel Krentzel; Julienne Petit; Yves‐Marie Boudehen; Nassim Mahtal; Elodie Sadowski; Agnès Zettor; Alexandra Aubry; Jeanne Chiaravalli; Nathalie Aulner; S. Petrella; Pedro M. Alzari; Christophe Zimmer; Anne Marie Wehenkel

doi:10.1126/sciadv.aeg6806

AI & machine learningAI for science & medicine

Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

Daniel Krentzel, Julienne Petit, Yves‐Marie Boudehen, Nassim Mahtal, Elodie Sadowski, Agnès Zettor, Alexandra Aubry, Jeanne Chiaravalli, Nathalie Aulner, S. Petrella, Pedro M. Alzari, Christophe Zimmer, Anne Marie Wehenkel

Peer-reviewed journalReal-world use

In the authors' words

This journal does not let us republish the full abstract. Here are the two sentences Pipette selected, quoted from it. Read the rest at the publisher.

Main result

Our model robustly classifies MoAs of established antibiotics and recognizes the MoA of previously unseen antibiotics.

Read the paper

Appeared: Sunday, September 27. Science Advances. Peer-reviewed journal.

DOI: 10.1126/sciadv.aeg6806

Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

In the authors' words

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