Source Code: Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria (v0.1.0)

Source code for 'Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria '.

By Krentzel, Daniel, Petit, Julienne, Boudehen, Yves-Marie, Mahtal, Nassim, Sadowski, Elodie, Zettor, Agnes, Aubry, Alexandra, Chiaravalli, Jeanne, Aulner, Nathalie, Petrella, Stephanie, Alzari, Pedro, Zimmer, Christophe, Wehenkel, Anne MarieToolReference information

Resource information

Format: zip
Original repository: 21359993
DOI: 10.5281/zenodo.21359993
Credit basis: Creators in Zenodo record metadata
Access: Open files available from the original Zenodo deposit
Last checked: Nov 5, 2026
License: mit-license
Last record update: Nov 5, 2026

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Source code for 'Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria '.