The first AI system for human embryonic state analysis is available for testing - Scienmag

"BioTime harnesses the largest collection of highest-quality gene expression data coming from scrupulously designed and controlled cell differentiation experiments we have seen to date. It was large enough to train a complex architecture of deep neural networks to work as a classifier and a predictor of the embryonic state. We recently tested Embryonic.AI using mouse data and noticed surprising results showing the capabilities of this system in cross-species analysis. Research projects using Embryonic.AI may transform our understanding of cancer and other diseases and possible developments in reinforcement learning may help navigate and control cellular differentiation states", said Alex Zhavoronkov, Ph.D., CEO of Insilico Medicine, Inc. The system utilizes a sophisticated architecture of multi-class deep neural networks (DNNs) and DNN ensembles trained on thousands of samples of carefully selected cells of multiple classes: embryonic stem cells, induced pluripotent stem cells, progenitor stem cells, adult stem cells and adult cells to recognize the class and embryonic state of the sample, achieving high accuracy in simulations.