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.

Dr. Eliezer Van Allen will describe how a patient’s cancer genome can be used to guide individualized treatment choices for precision medicine. He will examine how to identify treatment resistance mechanisms and show how certain phenotypic patterns can be paired with new modes of computation to further inform treatment decisions.

Dr. Charles Wang will address the need for guidance in selecting algorithms for accurate biological interpretations of varied data types acquired with different single-cell RNA sequencing platforms.

Dr. Noémie Elhadad will show how patient-record summation tools can help clinicians make sense of seemingly overwhelming amounts of patient data at the point of care.

Dr. Nicolas Fernandez will demonstrate the latest version of Clustergrammer2, a visualization tool that allows users to perform more refined clustering on cell lines and genes.

Dr. James Zou will present new computer vision algorithms and describe how these can help identify the morphologies and phenotypes associated with complex diseases, such as cancer. He will also discuss the general principles behind this technology as well as current machine learning tools, such as Ghorbani, NeurIPS 2020, Abid, and Nature MI 2020.