PROF, AST RESRCH - Saman Zeeshan
Saman Zeeshansaman.zeeshan@umkc.edu
Studying genetic insight with the application of AI/ML and state-of-the-art bioinformatics approaches can improve the processes of discovering cancer causing variants and decode genetics of complex cancer heterogeneity enabling improved personalized treatments. We have developed a model with a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing integrated clinical, demographic, and multi-omic data. Our model combines, knowledge-driven and data-driven approaches to create an AI engine that creates risk profile of each of the individuals that may help predict disease risk and possibly prevent disease occurrence. These studies challenge the paradigm that although there are efficient treatments, the use of AI is necessary to detect these factors in a longitudinal data for a given subject. The potential use of the prediction models goes beyond this research and can be applied to any disease.
In the laboratory we apply AI/ML models to discover new ways to address challenges in cancer health disparities. The lab has uncovered genetic factors associated with racial disparities in lung cancer, suggesting that biological factors may be a driving force and contribute to racial disparities among different races for various cancers. Breast cancer and prostate cancer have been the most frequently studied cancers for racial disparity. However, little is known about differences in lung cancer.
To determine which genetic factors are likely to play a role in lung cancer biology in different populations, using our AI/ML model we try to identify patterns, correlations, and predictive markers that are difficult to identify through traditional analyses and uncover hidden mechanisms and underpinnings of health disparities in lung cancer. AI can unveil these novel connections that challenge current paradigms, revealing new areas for research and interventions accelerating health disparities research.
Jun 10, 2024
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