Presented at the SLAS Advanced 3D Human Models and High-Content Analysis Symposium, London, UK
Genedata Imagence® allows for the application of deep networks to the analysis of High Content Imaging, creating a workflow that cuts image analysis time, increases data quality, reproducibility of results and seamlessly integrates with Genedata Screener® for image data analysis. This deep learning approach outperforms conventional approaches for feature extraction and phenotype classification. Here, LifeArc scientists present an evaluation of a recent pilot of Genedata Imagence for High Content Analysis. They compare the Genedata Imagence workflow with LifeArc's conventional in-house High Content Imaging and Analysis workflow through a series of assays that have been developed within the target validation biology group at LifeArc. The generation of training data and subsequent training of the network was carried out for each assay and results of the data analysis were compared to in-house conventional analysis.