Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.

ABSTRACT: Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. Deep learning algorithms obtain abstract representations of images through an automated process, allowing them to efficiently classify complex patterns, and have become the state-of-the art in machine learning for computer vision. Here, deep convolutional neural networks (CNN) were trained to predict toxicity from images of DAPI-stained cells pre-treated with a set of drugs with differing toxicity mechanisms. Different cropping strategies were used for training CNN models, the nuclei-cropping-b

SUBMITTER: Jimenez-Carretero D 

PROVIDER: S-EPMC6291153 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature