{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":[null],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD1752"],"repository":["bioimages"],"figure_sub":["Specimen","Image analysis","Annotations","Funding","Study Component","organisation","Biosample","Associations","Image acquisition"],"pubmed_authors":["Nicolas J. Longhi","Willam S. Noble","Madison Sanchez-Forman","Gang Li","Eva K. Nichols","Brian J. Beliveau","Conor K. Camplisson","Valentino E. Browning"],"additional_accession":[]},"is_claimable":false,"name":"Predicting cell cycle stage from 3D single-cell nuclear stained images.","description":"SUMMARY. Here, we provide the image datasets supporting the development of CellCycleNet, a cell cycle stage classifier tool. We imaged thousands of fixed interphase mouse fibroblast cells containing a Fluorescent Ubiquitination-based Cell Cycle Indicator-2a (Fucci-2a) transgene, stained with DAPI, from two common fluorescent microscope modalities: widefield epifluorescence and spinning-disk confocal microscopy.\n\nABSTRACT. The cell cycle governs proliferation of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. We developed CellCycleNet, a machine learning (ML) workflow, to simplify cell cycle staging from f","dates":{"release":"2025-03-27T00:00:00Z","modification":"2026-02-15T03:48:07.983Z","creation":"2025-03-27T16:14:49.666Z"},"accession":"S-BIAD1752","cross_references":{}}