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

ABSTRACT: Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non-linear least squares. Depending on the model, this can be time consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data are used for training different deep neural network regression models for predi

SUBMITTER: Wahlstrand Skarstrom V 

PROVIDER: S-EPMC8248438 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature