BioVis@ISMB 2026 Program

July 14, 2026

Detailed program to be announced.

Invited Speakers

The Visual Genome: An attempt to classify multi-omics visualization

Jean Fan, Johns Hopkins University, USA

Abstract: Advances in high-throughput spatial transcriptomics (ST) technologies enable high-throughput molecular profiling of cells while maintaining their spatial organization within tissues. Such high-throughput ST data demand new computational analyses and visualization approaches to identify and highlight genes that spatially change in their expression patterns between conditions, such as in diseased versus healthy tissues. In this talk, I will provide an overview of the latest ST computational analysis methods developed by my lab. In particular, to facilitate spatial molecular comparisons across structurally matched tissue sections from replicates, case-control settings, and within and across technologies, we previously develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. Likewise, to enhance the scalability of ST data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. More recently, we developed STcompare to integrate STalign and SEraster into a statistical framework for comparative analysis of ST data by testing for and visualizing differences in spatial correlation and spatial fold-change across structurally matched locations while robustly controls for false positives even in the presence of spatial autocorrelation common in ST data. Alternatively, to facilitate spatial molecular comparisons across structurally unmatched tissues, we previously developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, to quantify and visualize cell-type spatial relationships across multiple length scales. We have applied CRAWDAD to compare cell-type spatial organizations across samples as well as across functional tissue units within samples. Overall, we anticipate that such computational methods for analyzing and visualizing trends in ST data will contribute to important biological insights regarding spatial molecular changes across comparative axes of interest.

Speaker Bio: Jean Fan is an associate professor of Biomedical Engineering in the Center for Computational Biology at Johns Hopkins University. Her research team, the JEFworks lab, is interested in understanding the molecular and spatial-contextual factors shaping cellular identity and heterogeneity. She develops new open-source computational software for analyzing spatially-resolved multi-omic and imaging data that can be tailored and applied to diverse cancer types and biological systems. Dr. Fan is also the founder, director, and lead software developer for the non-profit organization CuSTEMized, which provides personalized STEM picture storybooks to encourage young girls to see themselves as scientists. She also serves as a Genomics section editor for PLoS Computational Biology. The impact of Dr. Fan’s work has been recognized by several awards and honors, including the Forbes 30 Under 30, the Nature Research Award for Inspiring Science, the NSF CAREER Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).

Healthy skepticism in AI: a BioVis research agenda

Liz Marai, University of Illinois Chicago, USA

Abstract: Data visualization for Artificial intelligence (AI) research has historically focused on enhancing trust through visual explanations of AI, under the assumption that humans are critical users and unlikely adopters of AI. It is becoming clear that, in reality, human trust-levels in AI span a wide range, from critical to nearly blind acceptance. This talk will describe my group’s work in developing AI-powered computational oncology models, with a focus on the benefits and risks of AI solutions. I will then argue that the data visualization field should support both trust and healthy skepticism in AI solutions, while also being especially equipped to make AI models better colleagues to the human.

Speaker bio: Liz Marai is a professor of Computer Science, and a designated University of Illinois Scholar. Marai’s research has been recognized by multiple prestigious awards, including a Test of Time Award, an NSF CAREER Award and several multi-site NSF and NIH awards as a lead investigator. She is the director of the UIC Institute for Health Data Science Research, and a chartered member of the US National Institutes of Health study section on clinical informatics and digital health. She has co-authored scientific open-source software adopted from Ghana to Canada, and she is an inventor whose ideas have been embedded into a medical instrument.