Fall 2026 Missouri Data Science and Informatics Symposium

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.