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.

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.