Data mining of spectroscopic data for biomarker discovery

The goals of precise diagnosis, prevention and treatment of disease can be realized through the discovery of biological markers. Spectroscopic tools can simultaneously detect and quantify multiple small molecule and macromolecular components of biological samples, and are therefore ideal methods for the discovery of previously uncharacterized markers. However, the identification of meaningful spectral features is complicated by the lack of foreknowledge of the molecular nature of a disease, spectral noise and biological variability that is uncorrelated with the disease state. Pattern recognition techniques, both statistical and machine-learning, have been increasingly used in recent years with spectroscopic data to identify markers and classify patients into disease subsets. This review summarizes recent developments, limitations and future prospects in the use of data mining techniques with magnetic resonance spectroscopy, mass spectrometry and optical spectroscopy for the discovery of biomarkers.