DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks

<p>Dataset and code used in V W&aring;hlstrand-Sk&auml;rstr&ouml;m, et al, &quot;DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks&quot;, published in Journal of Microscopy. In this work, we develop a new approach for FRAP analysis based on deep neural networks.&nbsp;From a numerical FRAP model developed in previous work, we generate a very large set of realistic, simulated recovery curve data. The data is used for training deep neural network regression&nbsp;models for prediction of e.g. the diffusion coefficient. We compare the performance of the neural network estimation framework to conventional least squares estimation on simulated and&nbsp;<br> experimental data. Herein, the simulated FRAP data used for the training, validation, and test data sets, the experimental data, and the Matlab and Python/Tensorflow code are supplied.</p>