Learn how researchers can use network models to understand the development and progression of diseases, such as cancer, and new ways to identify therapeutics.

During this webinar, New York University’s Dr. Itai Yanai will discuss the concept of cancer cell states present in a cancerous tumor prior to treatment, as well as those that accompany resistance to increasing drug doses.

Find out how longitudinal patient records impact cancer research, and how applying principles like transparency and reusability could improve this type of data!

Discover the results of the largest, real-world, federated learning study focusing on glioblastoma and leveraging data from thousands of patients across six continents. The speaker, Dr. Bakas, comes from the University of Pennsylvania’s Perelman School of Medicine.

Learn more about open-source artificial intelligence and machine learning models available to you!

Learn more about the safety and ethics of artificial intelligence in healthcare.

The NCI Center for Cancer Research’s Dr. Eytan Ruppin will present on “SELECT,” a computational approach that aims to identify clinically relevant synthetic lethal interactions and then harnesses them to predict patient response to cancer therapy from the bulk tumor transcriptome.

Dr. Obi L. Griffith will discuss CIViC, an open access, open source, community-driven web resource for clinical interpretation of variants in cancer. The goal of this resource is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations.

Dr. Svitlana Volkova will discuss a suite of augmented intelligence solutions powered by Artificial Intelligence models. She’ll show how these models can be used to detect, anticipate, and reason with technical expertise and capability development to analyze and extract knowledge from large amounts of unstructured scientific literature data.

Dr. Grassberger will highlight the current computational and mathematical approaches for modeling the impact of radiotherapy on the immune system, as well as designing the next generation of combination trials.