Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.

ABSTRACT: Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environmental perturbations. With a large number of images generated by high-content microscopy while manual labeling is both labor-intensive and error-prone, machine learning offers a viable alternative for automatic labeling of subcellular localizations. Contrarily, in recent years applications of deep learning methods to large datasets in natural images and other domains have become quite successful. An appeal of deep learning methods is that they can learn salient features from complicated data with little data preprocessing. For such purposes, we app

SUBMITTER: Xiao M 

PROVIDER: S-EPMC6416075 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature