From Single Cell Genomics to Drug Targets: Leveraging ML & AI to Discover Biological Insights
The rapid development of single cell genomics allows and the ability to understand expression level differences on a cell by cell level allows for the creation of novel precision therapies targeted toward specific subpopulations. As a tissue sample generates up to 10,000 cells and every cell can provide a read out on up to 20,000 genes, even a few hundred samples can easily generate tens of billions of data points. Identifying the signal within such large datasets requires the use of powerful machine learning algorithms throughout the entire computational biology process. In this talk we will cover the power of combining single cell genomics with machine learning in the drug discovery context and provide several examples of how we are doing this at Celsius Therapeutics. Gregory Ryslik is a statistician, data scientist and artificial intelligence researcher with experience building and leading data initiatives in companies ranging across the biotech, autotech, healthtech and fintech domains. Prior to Celsius Therapeutics, he was vice president of data science at Mindstrong Health, a healthcare company transforming mental health treatment through measurement science and artificial intelligence. Previously, Greg was the senior director and head of data science at Faraday Future, an electric vehicle startup in Los Angeles as well as the leader of the service data science group at Tesla Motors in Palo Alto. Earlier in his career, he performed machine learning research and nonclinical biostatistics research at Genentech.
Concurrently, Greg holds an adjunct assistant professor position at Pennsylvania State University and has lectured on statistics for artificial intelligence and machine learning at Stanford University Continuing Studies. He is also a fellow of the Casualty Actuarial Society, as well as a member of the American Academy of Actuaries. His research has been published in journals ranging from Nature to BMC Bioinformatics and has led to several software packages on mutational clustering.
Greg holds a Ph.D. from Yale University in biostatistics, where he researched oncogenic mutation clustering embedded within protein structure. He also holds a master’s degree in statistics from Columbia University and an undergraduate degree in mathematics, computer science and finance from Rutgers University.