Keywords: Prostate, Metabolism, Metabolomics, NMR
Motivation: Current diagnostic methods cannot predict the aggressiveness of prostate cancer (PCa) at a treatable stage of disease.
Goal(s): To interrogate tumorigenesis of PCa and AI/ML techniques to NMR-based targeted blood plasma metabolomic profiling analysis for prediction of PCa.
Approach: Use AI/ML approaches to NMR metabolic profiling for PCa patient blood plasma data analysis
Results: Phosphocreatine, choline, 3-hydroxybutyrate, taurine and glucose showed highest discriminate using CFS, PLS-DA, OPLS-DA, random forest models.
Impact: It will pave way to enhance understanding of cancer pathogenesis and biomarker/s identification and early detection systems.
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