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

Cellular/molecular imaging dataset — a durable, citable reference into the African DSI DataBank's federated catalog.

Last updated 10/12/2026, 8:01:53 PM

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African DSI DataBank. Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria. AFDSI-CELL-3059. 2026. https://hub.africandsidatabank.africa/cite/AFDSI-CELL-3059 Mirrored from data source: BioImage Archive: https://www.ebi.ac.uk/biostudies/studies/S-BIAD3680.Copy citation

@misc{AFDSICELL3059,
  title        = {Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria},
  author       = {{African DSI DataBank}},
  year         = {2026},
  howpublished = {\url{https://hub.africandsidatabank.africa/cite/AFDSI-CELL-3059}},
  note         = {African DSI DataBank citable identifier AFDSI-CELL-3059; Mirrored from data source: BioImage Archive: https://www.ebi.ac.uk/biostudies/studies/S-BIAD3680}
}

AFDSI-CELL-3059 is a stable, resolvable local identifier — not a registered DOI. See Terms of use's "Citation policy" for the full explanation.