Krentzel, D., Petit, J., Boudehen, Y., Mahtal, N., Sadowski, E., Zettor, A., . . . ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016). (2026). Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria. https://pasteur.hal.science/pasteur-05549423 ; 2026. https://doi.org/10.64898/2026.02.23.707449
Παραπομπή σε μορφή Chicago (17η εκδ.)Krentzel, Daniel, et al. "Deep Learning Extracts MoA-specific Signatures from High-throughput Images of Chemically and Genetically Perturbed Corynebacteria." Https://pasteur.hal.science/pasteur-05549423 ; 2026 2026. https://doi.org/10.64898/2026.02.23.707449.
Παραπομπή σε μορφή MLA (9th εκδ.)Krentzel, Daniel, et al. "Deep Learning Extracts MoA-specific Signatures from High-throughput Images of Chemically and Genetically Perturbed Corynebacteria." Https://pasteur.hal.science/pasteur-05549423 ; 2026, 2026, https://doi.org/10.64898/2026.02.23.707449.