Speaker Profile
Ph.D., Co-founder & CEO, Data4Cure
Biography
Janusz Dutkowski is Co-founder and CEO of Data4Cure, Inc. – a company focused on combining systems biology, machine learning and AI to facilitate continuous translation of biomedical data to knowledge. His background is in mathematics and computer science. For the last 15 years he has been working at the intersection of mathematics, computer science and biology to develop new data-driven technologies to advance the discovery of new biomarkers and precision-based therapies. Before founding Data4Cure he was a scientist at the University of California, San Diego where along with Dr. Trey Ideker he led the development of methods for multiscale analysis of molecular networks and integrative methods for biomarker discovery from multidimensional data. He has co-authored over 20 research papers published in scientific journals including Nature Biotechnology, Science and Cell.
Talk
Combining Systems Biology, Machine Learning and Evolution to Improve IO Biomarkers
A key challenge in the field of immunotherapy is predicting which patients will respond to IO therapy either as a single agent or in combination with other treatments. Existing clinical and experimental strategies involve testing expression of individual markers or gene panels, assessing tumor mutation burden, and more recently, approaches examining specific tumor neoantigens in the context of tumor evolution. How effective is each approach individually and how does it fit into a comprehensive picture of thetumor-immune interaction system? Can a systems view be more predictive than each component independently? We will discuss some of the new methods and strategies that can help address these questions.
Presentation Title and Company Description
AI and Data Science Showcase: Using Systems Biology and AI to Continuously Grow Biomedical Knowledge
DATA4CURE’s Biomedical Intelligence® Cloud combines systems biology with an advanced semantic AI integration engine to turn big omics and clinical data into a continuously updated biomedical knowledge graph. By actively mining thousands of public and proprietary datasets and millions of biomedical publications this technology discovers genes, pathways and networks underlying disease and treatment response, merging diverse pieces of evidence to increase statistical power and facilitating continuous translation of data to knowledge. Leading pharmaceutical companies use the platform every day to search for new targets, repurpose drugs for new indications, identify disease subtypes, and predict drug response and resistance in patients.
