The first AI system for human embryonic state analysis is available for testing
Tuesday, July 5, 2016, Baltimore, MD – At Mensa Annual Gathering 2016, the annual event of the largest and oldest high IQ society transpiring in San Diego from June 29th to July 3rd, Dr. Michael West, CEO of BioTime, Inc announced the launch of a beta version of Embryonic.AI, an artificially intelligent system for analyzing the embryonic state of human cell samples using gene expression data. The first implementation of Embryonic.AI was launched by LifeMap Discovery, Inc, a subsidiary of BioTime, Inc and is freely available for beta testing. Scientists and companies from all over the world are welcome to test their stem cell and adult cell samples using Embryonic.AI and discuss collaboration alternatives. A brief video provides a general introduction to the Embryonic.AI system is available at https://www.youtube.com/watch?v=MbgZSDqPe78 and BioTime's press release is available at http://finance.yahoo.com/news/BioTime-presents-online-applying-artificial-120000254.html
"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. The unique samples were generated using standardized protocols by BioTime, Inc. and profiled on a single microarray platform. The sample sets were augmented with carefully selected and manually curated data from public repositories coming from multiple experiments and generated on different platforms.
To design and implement the DNN architectures, BioTime partnered with the Pharmaceutical Artificial Intelligence (Pharma.AI) division of Insilico Medicine, Inc., a Baltimore-based bioinformatics company specializing in biomarker and drug development for aging and age-related diseases. There are many potential applications of this system in multiple areas ranging from cancer research and quality control to in vivo regeneration. Embryonic.AI may have future applications in comparing multiple biopsies of patients' tumor to search for cancer stem cells. One of the major challenges in organ engineering for drug testing is quality control of the engineered human tissues to ensure that it closely resembles the expected results. Embryonic.AI may have future applications in analyzing the embryonic state of these tissues and evaluating the effectiveness of many drugs in a high-throughput manner.
Recently Insilico Medicine published several key papers on applying deep learning techniques to biomedical applications in influential peer-reviewed journals including "Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data" in "Molecular Pharmaceutics" and ACS publication. The paper received the Editors' Choice Award from the American Chemical Society. "Applications of Deep Learning in Biomedicine" in also in Molecular Pharmaceutics and "Deep biomarkers of human aging: Application of deep neural networks to biomarker development" in Aging, one of the highest-impact journals in aging research. These studies were presented at the Machine Intelligence Summit Berlin on June 30th.
About Insilico Medicine
Insilico Medicine, Inc. is a bioinformatics company located at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore with R&D resources in Belgium, Russia, and Poland hiring talent through hackathons and competitions. The company aims to transform drug discovery and development by rapidly generating and validating thousands of new leads for multiple diseases and developing novel biomarkers using a technique called deep learning. The company pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery. Through its Pharma.AI division, the company provides advanced machine learning services to biotechnology, pharmaceutical and skin care companies. Since 2014 company scientists published over 40 research papers in peer-reviewed journals and collaborated with over 150 academics, biotechnology and pharmaceutical companies worldwide. Brief company video: https://www.youtube.com/watch?v=l62jlwgL3v8