Abstract

(A) Pipeline through which data are prepared for training and testing the deep network for SNAP25 from 48-hour protocol as an example. Ten-dimensional vectors containing pixel-wise intensities across all channels are mapped along one dimension using kPCA transform. A relative threshold on the principal component separates foreground from background and results in a binary mask, based on which data can be gathered from points than contain proteins in the confocal image. (B) The result of Isomap, kPCA, t-SNE, and Sepctral Embedding “shallow-learning” methods for dimensionality reduction applied directly to the data gathered according to the pipeline explained in (A). (C) Training and validation accuracies averaged over all proteins in the dataset, sampled in each training epoch. Red dashed line shows the early stopping used based on the monitored validation accuracy. (D) Results of the ablation study, in which in each case one protein is removed from the training dataset and the performance of the deep network is evaluated based on the given metrics after training and validation procedure is performed. The data underlying this Figure are available as file “Fig 3_ABCD.xlsx” from http://dx.doi.org/10.17169/refubium-40101.</p

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