Rafael leads Genialis company’s effort to model patient biology to realize the promise of precision medicine and therapeutic discovery. He spent nearly 20 years in biomedical research prior to Genialis, publishing on the evolution of innate immune systems, bioengineering of microbes, and genetics of development. He has also nurtured a specialty in developing software for high-throughput molecular design and analyses, co-inventing the j5 DNA assembly design automation tool (which has since been commercialized by TeselaGen Biotechnology). Rafael attended Dartmouth College and then Yale University, where he was an NSF Graduate Research Fellow. He went on to postdoctoral training in Jay Keasling’s synthetic biology group at Lawrence Berkeley National Laboratory, Joint BioEnergy Institute (JBEI), followed by a National Library of Medicine Keck fellowship in Biomedical Informatics at Baylor College of Medicine. In his free time, Rafael enjoys cooking and rock climbing, and raising heirloom tomatoes and two precocious children.
AI and Data Sciences Showcase:
Genialis is a computational precision medicines company unravelling complex biology to find new ways to address disease. ResponderID™, Genialis’ clinical biomarker discovery platform, defines, models, and validates actionable biomarkers and optimally positions novel drugs to accelerate translational research and clinical development.
Machine-Learning for Clinical Biomarker Discovery
How can we achieve clinic-ready biomarker algorithms from messy and small patient data sets? Come see.
Session Abstract – PMWC 2023 Silicon Valley
The PMWC 2023 AI & Data Sciences company Showcase will provide a 15-minute time slot for selected companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.