AI and Machine Learning with Advanced Preclinical Cancer Models for Improved Clinical Translation

As researchers look to leverage genotypic features of models for early identification of predictive biomarkers, understand complex immune interactions, and elucidate combination therapies’ mechanisms of action, it’s becoming apparent that the fight against cancer will be won at the intersection of biology, biochemistry, and computer science.

During this webinar, Certis Oncology Senior Director, Scientific Engagement and Key Accounts Michael Boice, PhD, looks at how in silico predictions of efficacy, when validated in more clinically relevant preclinical models, can bring greater certainty to early decision-making, and improve translation to the clinic. He also demonstrates how in vivo validation studies can accelerate AI and machine learning, rapidly improving the accuracy of compound-specific in silico models for biomarker optimization. CertisAI™ Expert Panelist Long H. Do, PhD, Director of Bioinformatics and Data Science, joins for the Q&A session.