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 about a new cluster analysis model that provides insights into microbiome networks.

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

Connect with Boston University’s Drs. Beane-Ebel and Kolachalama as they discuss their studies of the pathological and molecular alterations associated with lung lesion severity and progression. They will examine deep learning methods for characterizing the pathology of premalignant lesions and lung cancer specimens.

Hear from Emory University’s Dr. Madabhushi as he discusses how radiomic and pathomic approaches can be applied to predicting disease outcome, recurrence, progression, and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers.

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

Hear from a biomedical informatics professor as he highlights the approaches for making public -omics data more accessible as well as the metadata challenges he and his research group have encountered along the way.

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.