Join Dr. Michael Boice from Certis Oncology for a discussion and demonstration of how artificial intelligence can improve preclinical decision-making in cancer research.
Molecular profiling and other tumor model characterization data are becoming increasingly important to preclinical decision-making. During this presentation, you’ll experience first-hand how the latest in AI/ML tools are revolutionizing cancer research and early drug development by:
- Optimizing preclinical model selection.
- Informing biomarker, drug repurposing, and label expansion strategies.
- Enabling quick in silico analysis of complex datasets to provide insights into therapeutic efficacy and drug response.
- Simplifying data retrieval and enable efficient, ad hoc analyses for research teams.
Eligible attendees will receive access to an exciting new generative AI tool developed exclusively for cancer researchers.
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Presenters
Senior Director
Scientific Engagement and Key Accounts
Certis Oncology Solutions
Michael Boice, PhD, has over 20 years of experience in translational oncology drug development. Michael’s areas of scientific expertise include the development of novel oncology therapeutics, functional genomics and rare cancers. At Certis, he works directly with clients to enhance their discovery efforts through translational science discussions, optimizing the customer experience. He holds a PhD in Pharmacology from Weill Cornell Graduate School of Medical Sciences, Department of Pharmacology Memorial Sloan Kettering Cancer Center, Cancer Biology and Genetics.
Production Partner
Certis Oncology Solutions, Inc.
Certis is an AI-enabled precision oncology and translational science company. Our product is Oncology Intelligence®—highly predictive therapeutic response data derived from advanced models of cancer. We partner with oncology therapeutics developers to help close the problematic translation gap between preclinical studies and clinical trials. Using CertisAI™, our proprietary artificial intelligence/machine learning platform, and more clinically relevant, patient-derived tumor models, we bring certainty to lead candidate selection and help secure clear and compelling evidence of drug efficacy.
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