Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture

Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image...

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Bibliographic Details
Published in:arXiv.org 2019-08
Main Authors: Kormilitzin, Andrey, Yang, Xinyu, Stone, William H, Woffindale, Caroline, Nicholls, Francesca, Ribe, Elena, Nevado-Holgado, Alejo, Buckley, Noel
Format: Article
Language:English
Subjects:
Artificial neural networks
Biological activity
Chemical compounds
Classification
Deep learning
Object recognition
Organic chemistry
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Summary:Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image recognition model which is based on deep convolutional neural network (CNN) architecture with residual connections achieved accuracy of 99.6\(\%\) on a binary classification task of distinguishing untreated and treated rodent primary neuronal cells with Amyloid-\(\beta_{(25-35)}\).
ISSN:2331-8422