Fall 2026 Missouri Data Science and Informatics Symposium

More than 2 in 5 American adults (136 million people) have prediabetes, which places them at increased risk for developing type-2 diabetes (T2D) and cardiovascular disease (CVD). Dietary polyphenols in plant foods are associated with reduced risk and symptoms of chronic metabolic disease, but only about 10% of US adults consume the recommended daily servings of fruits and vegetables. Our preclinical murine studies have shown that Concord grape polyphenols (GPs), rich in the proanthocyanidin (PAC) class of compounds, can promote metabolic benefits at least in part by altering specific gut microbes and bile acids involved in glucose homeostasis. We performed a longitudinal, single arm intervention study to investigate how our murine data would translate to humans (NCT04018066). Metabolomic, metagenomic, and metaproteomic changes were measured in healthy participants (n= 27) before and after 5 days of soy protein isolate (SPI) supplementation alone followed by 10 days of supplementation with GPs complexed to SPI (GP-SPI). Serum, fecal, and urine samples were collected before and during the 17 day study and prepared for shotgun metagenomic sequencing, mass spectrometry-based metaproteomics, and targeted metabolomics (i.e., bile acids and polyphenols metabolites). Most multi-omic changes observed after 2 and/or 4 days of GP-SPI intake were temporary, returning to pre-supplementation profiles by day 10, suggesting microbial adaptation to PAC-rich GPs. Notably, 10 days of GP-SPI supplementation decreased fasting blood glucose in association with increased serum hyocholic acid (HCA), a bile acid known to promote glucose homeostasis through increasing glucagon-like peptide 1 (GLP-1). While causal relationships remain to be investigated, to our knowledge, this is the first study linking intake of GPs (mainly PACs) with increased serum HCA species, providing a novel mechanism by which GPs may contribute to glucose homeostasis.

AI for Biomedical Knowledge Discovery: Integrating Literature, Molecular, and Clinical Data


The rapid growth of biomedical data across scientific literature, molecular profiling, and clinical records presents both unprecedented opportunities and significant challenges for knowledge discovery. In this talk, I present a unified perspective on how artificial intelligence can help uncover meaningful insights across heterogeneous biomedical data modalities. Drawing on selected projects from my doctoral research, I highlight three examples spanning different scales of analysis. First, I discuss biomedical literature mining approaches that integrate image and text data to extract structured knowledge from pathway figures and publications. Second, I present recent work on fine-tuning foundation models for single-cell data to improve biological representation learning and cross-dataset generalization. Finally, I describe ongoing clinical NLP research that combines large language models with knowledge graphs to support reasoning over real-world clinical narratives. Together, these examples illustrate how AI methods can bridge fragmented biomedical information and support interdisciplinary discovery across research and clinical domains. I conclude with reflections on emerging opportunities for more interpretable and translational AI systems.

Canine malignant melanoma (CMM) mimics human melanoma in invasiveness, metastatic behavior, and limited survival. Novel immunotherapies could target CMM neoantigen expression to dissect mechanisms of success and failure in a naturally occurring model of cancer. Tools to predict and validate canine MHC I presentation of neoantigens must be optimized to select candidate vaccine peptides.
Tissues and a cell line from a dog responding in a trial of an autologous deglycosylated melanoma vaccine were selected. Nucleic acids were extracted for WES and RNAseq. Expressed protein-coding sequence mutations were identified by strelka, varscan, mutect, and pindel. DLA allele was inferred by pseudo-alignment of RNA against known sequences and confirmed by clinical assay. Candidate neoantigens were identified with pVACtools. Membrane-bound MHC I was purified using mAb H58A. Weak acid-eluted MHC I- bound peptides were analyzed with mass spectrometry, and peptide sequences inferred by MSGFplus.
Neoantigens were predicted with strong binding affinity to DLA 88*002:01. Peptides eluted ranged from 7-16 AA with nonamers being the mode and the vast majority ranging from 9-12 AA. Candidate neoantigen sequences were compared to eluted peptides and vaccine candidates selected with highest predicted binding affinity and strongest support in the MS data.
Human MHC I typing and binding affinity software tools can be modified to predict canine MHC I binding affinity. Predicted neoantigens were confirmed by presence in peptides eluted from cells derived from the patient’s tumor. These tools will be used in future immunotherapy studies in companion dogs with melanoma as a model for humans.

Healthcare and biomedical research generate data at unprecedented volume and complexity, yet translating these data into actionable decisions remains a fundamental challenge at every level, from the bedside to research policy. In this talk, I present a series of our studies illustrating how data science methods, including machine learning, natural language processing, computable phenotyping, and network analysis, can serve as a decision engine across diverse domains. This shows how computable phenotype analyses within a national clinical research network reveal meaningful variation in medication adherence by patient groups, disease stage, and rurality; how explainable machine learning models can improve prognostic risk stratification in heart disease and cardiac imaging; and how foundation transformer models and large language models are reshaping diagnostics and patient communication. Beyond the clinic, I discuss solutions for data interoperability and harmonization in genomic and precision medicine, and present text-mining and network analyses that uncover hidden patterns in NIH funding, relevant to anyone invested in the future of research funding. Together, these projects illustrate a unifying principle: that rigorous, transparent data science can strengthen decision-making not only in how we diagnose and treat disease, but also in how we communicate findings to patients, harmonize data across systems, and allocate research resources.

This lecture will provide an overview of history, challenges, current state, and future potential of AI in healthcare from the perspective of a neuroradiology physician-scientist. Recent technical advances including foundation models, generative AI, agentic AI, and quantum computing, offer novel approaches to complex problem-solving in healthcare. However, medical technology implementation is constrained by several parameters including patient safety, privacy, and market considerations. Clinical AI development requires a clear understanding of intended use cases, data quality, computing power, and postmarket surveillance. We will investigate clinical and research applications in radiology, which has led the medical AI revolution with routine storage of massive digital datasets used for training large ML/DL models. Several clinical AI solutions are available for image acquisition, enhancement, detection, classification, diagnosis, and clinical decision support. However, current FDA-cleared tools represent narrow models that lack generalizability and adaptability. With the advent of GAI, we are seeing broader relevance to multimodal datasets and multitask applications in various domains. As AI technologies increasingly permeate clinical care, strong legal and governance frameworks are needed to mitigate failure modes and provide human oversight. We will highlight promising research collaborations with MU IDSI in health informatics, bioinformatics, and geoinformatics. Emerging technologies and transdisciplinary interactions offer exciting opportunities to reimagine the future of healthcare with evolutionary multi-omics, precision theranostics, human–robot collaboration, medical metaverse, and multi-agent superintelligence.

That wasn't the plan: Leveraging Cancer Surveillance Infrastructure for Public Health Impact

Not every career follows a straight line, and in health informatics, that divergence can reveal unexpected areas of impact. Using the Missouri Cancer Registry and Research Center as an example to show how cancer registries evolved from passive data collection into foundational public health infrastructure, and demonstrating how data linkage, systems integration, and advanced analytics transform longitudinal, population-level data into actionable intelligence. With a One Health perspective, cancer surveillance becomes a platform for precision public health, integrating clinical, environmental, and social data, to support informed decision-making and policies.

Theme: “Empowering One Health: AI, Data Science & Novel Alternative Methods”