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.

Dr. Malachi Griffith will give an introduction to neoantigens, a promising area for cancer immunotherapy and precision medicine. Dr. Griffith will describe how immunogenomic and bioinformatic approaches are helping to identify neoantigens and therapeutic modalities to target these abnormal proteins. He also will offer insight into tools to support related clinical trial efforts.

Dr. Ben Raphael will describe how tumors are heterogeneous mixtures of normal and cancerous cells with distinct genetic and transcriptional profiles. He’ll also discuss computational approaches to quantify tumor heterogeneity and reconstruct tumor evolution using data from single-cell DNA and spatial RNA sequencing technologies.

Dr. Elana J. Fertig (whose lab also uses multiplatform genomics data, blended with mathematical modeling and artificial intelligence) will demonstrate how CoGAPS, an established method for identifying transcriptional signatures related to cell type and state, can be applied to single cell data to identify patterns underlying immunotherapy response and resistance. Combining CoGAPS with transfer learning approaches provides insight into the patterns found in preclinical models, which then can be applied to patient samples to find shared features across data sets from different species.

In this talk, Mrs. Aya Abdelsalam Ismail will give an overview on interpreting Deep Neural Networks (DNNs), focusing on the use of DNNs to track and predict changes over time.

In this talk, Dr. James Lacey will describe the choices the cancer research community should consider and the questions they need to ask when transitioning their research projects to a cloud-based ecosystem.

In this talk, Dr. David Kepplinger will describe the detrimental effects of “data-artifacts,” specifically as they relate to biomarker discovery and related feature selection techniques. He will also discuss a novel method for reliably identifying relevant biomarkers in the presence of such artifacts.

Dr. Deborah Guadalupe Duran will define social determinants of health (SDOH). More specifically, she will give examples of how SDOH influence health outcomes; describe challenges related to collecting and using SDOH data; and offer resources for additional information on SDOH. She also will provide/suggest a new socio-cultural approach for including SDOH in biomedical research and policies.