Fall 2026 Missouri Data Science and Informatics Symposium

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.

Callery pear was detected throughout Columbia, MO using high resolution (3m) imagery and machine learning. A new model was then developed to further distinguish between planted individuals and escaped populations with a high rate of accuracy (AUC > 0.9). Using these detections, we trained an XGboost model to identify socio-economic and environmental factors associated with each population type (AUC > 0.8). Divergent sets of predictors were influential for planted and escaped Callery pear tree populations. While both populations were influenced by environmental variables (e.g., fractional imperviousness, soil pH, flooding frequency), planted populations were also shaped by socio-economic variables (e.g., population density, vacancy rate, median household income) while socio-economic associations with escaped populations were not as strong. To predict areas vulnerable to invasion in the future, we modeled urbanization changes to the year 2050 assuming slow, average, or fast rates of urbanization (AUC > 0.85). Using our previously trained XGBoost model, we then predicted areas that would be most vulnerable to invasion based on the urban modeling for each scenario. These findings demonstrate the value of integrating spatial analysis and machine learning to examine invasion dynamics and support proactive management of urban invasive species.

Early identification of mild cognitive impairment (MCI), fall risk, and frailty is critical for preventing cognitive and functional decline in aging populations. Sensor-based motor assessments combined with machine learning (ML) provide promising approaches for detecting early functional changes. This study aimed to design and evaluate an explainable machine learning (ML) framework that integrates sensor-derived motor features with demographic and clinical data to identify early indicators of these conditions. Eighty-three community-dwelling adults (≥60 years) completed multimodal motor assessments using the Mizzou Point-of-Care Assessment System (MPASS), which captures synchronized gait, balance, and sit-to-stand performance. Sensor-derived motor features were extracted from these assessments and combined with demographic and clinical variables to develop predictive models using XGBoost and Decision Tree algorithms. In addition, a unified multilabel modeling framework using XGBoost, Decision Tree, and AdaBoost was implemented to simultaneously predict MCI, fall risk, and frailty. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP) to identify key motor-function predictors and enhance transparency in model decision-making. By integrating explainable ML with multimodal motor-function data, this framework provides a data-driven approach for detecting early functional changes associated with cognitive and physical decline. The findings highlight the potential of combining sensor-based motor assessments with interpretable ML techniques to support scalable and transparent early screening strategies in aging populations.