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Fig. 7 | BMC Biology

Fig. 7

From: Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock

Fig. 7

Biological status of a nematode can be predicted based on PVD neuron’s health. a Average of mean bead size vs. the average number of beads for young and aged nematodes. Age-induced PVD degeneration patterns are complex, and two metrics are not sufficient to accurately classify the two populations. b Principal component analysis (PCA) for young and aged adults does not enable distinguishing young and aged groups, based on the two first principal components. c Schematic of the pipeline for computer-based machine learning models to predict the nematode’s biological status based on the morphological structure of PVD. Raw images are fed to Mask R-CNN algorithm to obtain binary mask, which is then used to extract the 46 metrics. Multiple models were trained based on these 46 metrics and tested on separate datasets. df Classification accuracy for young vs. aged, cold-shocked vs. control, and cold-shocked vs. aged nematodes. NT young = 50, NT old = 100, NV young = 10, and NV old = 20 for young vs. aged classification. NT cold-shocked = 75, NT control = 75, NV cold-shocked = 15, and NV control = 15 for cold-shock vs. control classification. NT cold-shocked = 75, NT aged = 100, NV cold-shocked = 15, and NV aged = 20 for cold-shocked vs. aged classification. (NT, numbers for training set; NV, numbers for validation set). SDE, subspace discriminant ensemble; KNN, K-nearest neighbor; SVM, support vector machine

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