bioRxivBioinformaticsPreprint
BEHAV3D-Explorer enables natural language-assisted profiling of 3D cellular dynamics
Sam de BlankiD, Emilio Rios-JimeneziD, Alejandra Rubio-Munoz, Oliver Hoffman, Britt M. T. Vervoort, Amber K. L. WezenaariD, Jessica K. Mazalo, Miguel Molina-Moreno, Hsin-Fang Chang, Ellen J. WehrensiD, Anne C. RiosiD, Maria Alieva
Abstract
Obtaining quantitative measurements from live 3D microscopy remains difficult to implement across research settings, particularly without profound image-analysis expertise. We present BEHAV3D-Explorer, an interactive, user-friendly platform that uses natural-language guidance to help users select analysis strategies, tune parameters and configure workflows. BEHAV3D-Explorer enables time-resolved behavioral classification and integrates it with morphological, interaction and functional analyses, thereby connecting dynamic readouts to yield interpretable results across imaging applications.
- assay: imaging
- assay: fluorescence / live-cell microscopy
- research focus: cell signaling & trafficking
- research focus: clustering
- research focus: cell type annotation
- approach: deep learning
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