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Part 2: A Novel Bayesian Approach for Uncovering Potential Spectroscopic Counterparts for Clinical Variables in 1H NMR Metabonomic Applications
This video was recorded at Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB), Tuusula 2006. Metabonomic approaches based on spectroscopic data are in their infancy in biomedicine. A key challenge in clinical metabonomics is uncovering and understanding the relations between the multidimensional spectroscopic data and the clinical measures currently used for disease risk assessment and diagnostics. A novel Bayesian approach for revealing clinically relevant signals is presented here for a real 1H NMR metabonomics data set. The results are not only mathematically superior but also biochemically fully coherent.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
