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Figure 1

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ZDB-FIG-210708-5

Publication

Yang et al., 2021 - Zebrafish behavior feature recognition using three-dimensional tracking and machine learning

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Figure 1

Flowchart illustrating the experimental strategy of this study. The experimental workflow included recorded novel tank test behaviors across treatments and trials. A video-tracking analysis was performed and 3D swimming path reconstruction was performed by using customized MATLAB and R software. Time-segmentation and dimensionality reduction of the 3D spatiotemporal data were performed by a principal component analysis (PCA), followed by unsupervised clustering by using customized machine learning algorithms (FuzzyART) across all behavioral spatiotemporal data to identify potential behavior features linked to treatment. Finally, a behavior feature evaluation was performed with the trained model and new input data.

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