Part 2: A Novel Bayesian Approach for Uncovering Potential Spectroscopic Counterparts for Clinical Variables in 1H NMR Metabonomic Applications

Posted in Science on September 08, 2008


Part 2: A Novel Bayesian Approach for Uncovering Potential Spectroscopic Counterparts for Clinical Variables in 1H NMR Metabonomic Applications

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.

Author: Ville Petteri Mäkinen, Helsinki University Of Technology Coauthor: Mika Ala Korpela, Lappeenranta University Of Technology

Watch Video

Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Bayesian Learning