(Eagan, MN) — Jan. 12, 2023 — Predictive Oncology Inc. (NASDAQ: POAI) today announced the appointment of Robert F. Murphy, Ph.D., to the company’s Scientific Advisory Board where he will serve alongside other key thought leaders in their respective fields to guide the company’s scientific initiatives and growth strategy.
As a pioneer in the field of machine learning and analytics for biological data, Dr. Murphy is an expert in developing algorithms and models to understand biological systems and relationships. He was founding head of the Computational Biology Department at Carnegie Mellon University (CMU), and led the development of CORE™, the machine learning technology that is exclusive to and powers Predictive Oncology’s PEDAL platform.
“Dr. Murphy brings a depth of knowledge that allows us to expand the boundaries of scientific research and how we can use our technology and domain expertise to generate proprietary data for our partners. His experience with ground-breaking research is vital to the evolution of our predictive models and goal to discover more effective cancer therapies,” said Raymond F. Vennare, Predictive Oncology Chief Executive Officer and Chairman of the Board.
Predictive Oncology is changing the landscape of oncology drug discovery with a new category that goes beyond artificial intelligence. By paring the machine learning of CORE with a biobank of more than 150K tumor samples, the company has developed a solution with its PEDAL platform that can improve the probability of success for biopharma partners.
“I am committed to the mission and the impact we can have in advancing the development of therapies that are more effective at fighting cancer. Because of the continuous iterative development of complex predictive models and the power that generating data against a diverse patient population provides to evaluating a given therapy, Predictive Oncology has the ability to advance molecules to medicine with better confidence. I am humbled by the honor to be a part of that process,” noted Dr. Murphy.
In addition to his role as head of Computational Biology Department at CMU, Dr. Murphy has also served as Professor of Biological Sciences, Biomedical Engineering, and Machine Learning there. He is an Honorary Professor of Biology at the Albert Ludwig University of Freiburg, Germany, and was the recipient of an Alexander von Humboldt Foundation Senior Research Award. He is a Fellow of the Institute of Electrical and Electronics Engineers and the American Institute for Medical and Biological Engineering, and served as President of the International Society for Advancement of Cytometry. He was the first full-term chair of the Biodata Management and Analysis Study Section of the National Institutes of Health and was a member of the National Advisory General Medical Sciences Council, and the National Institutes of Health Council of Councils.
Among his many accolades, Dr. Murphy has a patent for identifying location biomarkers, published more than 200 research papers and served on numerous editorial boards, committees, advisory panels, conference organizations and committee panels and professional societies.
Dr. Murphy received an A.B. in Biochemistry from Columbia College and a Ph.D. in Biochemistry from the California Institute of Technology. He was a Damon Runyon-Walter Winchell Cancer Foundation Postdoctoral Fellow with Dr. Charles Cantor at Columbia University.
About Predictive Oncology
As a science-driven company on the leading edge of oncology drug discovery, Predictive Oncology (NASDAQ: POAI) offers an unrivaled suite of solutions for the biopharma industry. Through the integration of scientific rigor and machine learning, the company has developed the ability to advance molecules into medicine more confidently by introducing human diversity earlier into the discovery process with the pairing of artificial intelligence and the world’s largest privately held biobank of over 150K tumor samples. Predictive Oncology’s solutions additionally include tumor models, biologics development, formulation design, a GMP facility, a CLIA laboratory and substantial scientific domain expertise.