Impact AI Targeted Cancer Treatments with Rafael Rosengarten from Genialis
New research and technology are radically transforming cancer treatment, and, today, we find out how. I am joined by Genialis CEO and Co-Founder, Rafael Rosengarten to discuss his company’s mission to “outsmart cancer.” Genialis is revolutionizing cancer care by developing AI models that decode the biology behind different types of cancer and identify the most effective therapies for individual patients.
In this episode, we discover how Genialis’ innovative approach of turning RNA sequencing data into tumor phenotype classification is remolding the landscape of precision medicine. Rafael explains the company’s methods of handling the high dimensionality and sparseness of sequencing data while addressing bias issues, filling us in on why they use shallower artificial intelligence architectures for algorithm training and more. Join us as we explore the cutting-edge world of personalized cancer treatments that are shaping the future of oncology.
Key Points:
- Genialis CEO and Co-Founder, Rafael Rosengarten’s background; what led him to Genialis.
- How Genialis applies machine learning to help patients find personalized cancer treatments.
- Their collaboration with drug and diagnostics companies to deploy their models.
- How the models use RNA sequencing data to predict and classify tumor phenotypes.
- The challenges encountered when training models with sequencing data.
- Rafael defines sequencing data.
- Why RNA sequencing for clinical applications is considered cutting-edge.
- Genialis’ methods for handling the high dimensionality and sparseness of sequencing data.
- The various sources of bias and how they have addressed these issues.
- Why they use shallower artificial intelligence architectures for algorithm training.
- How the FDA’s regulatory process affects how Genialis develops and validates its models.
- The benefits the Genialis team has seen from publishing research articles.
- How they measure the impact of their technology.
- Rafael’s advice to other leaders of AI-powered startups.
- The hyper-commoditization of AI technologies.
- Rafael predicts the future impact of Genialis and shares his goals for the company.
Quotes:
“[Genialis applies] machine learning to try to help patients find the best drugs for their disease, to help realize the promise of precision medicine.” — Rafael Rosengarten
“The models are learning the fundamental biological nature of the disease. From that, we can extrapolate what the best intervention will be.” — Rafael Rosengarten
“Not all genes have detectable expression at once. Certainly, not all genes are going to be informative. We've built really beautiful software that allows us to aggregate these kinds of sequencing data, to process them in a very uniform way.” — Rafael Rosengarten
“It really is a pan-cancer model, even though it was trained on a data set that was just gastric cancer. And it works on RNA sequencing of all different chemistries, even though it was trained on microarray” — Rafael Rosengarten
“The key with algorithm training, of course, is to try to avoid what's known as overfitting.” — Rafael Rosengarten
“Every phenotype that our model predicts whether it's phenotype A, B, C, or D, has a different therapeutic hypothesis.” — Rafael Rosengarten
“AI technologies right now are becoming hyper-commoditized.” — Rafael Rosengarten
“It is still possible for small companies to come up with really innovative algorithms — but for the most part, it really matters how you deploy these technologies.” — Rafael Rosengarten
Links:
Rafael Rosengarten on LinkedIn
Talking Precision Medicine Podcast
Resources for Computer Vision Teams:
LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.