DATA MINING AND OPTIMIZATION APPLIED TO RAMAN SPECTROSCOPY FOR ONCOLOGY APPLICATIONS

Recent advances in Raman spectroscopy have generated a surge of interest in biomedical applications particularly in the field of oncology. As cancer is predicted to become the number one cause of death by the end of the decade, Raman spectroscopy has the potential to significantly aid in the research, diagnosis and treatment of cancer. Biomedical applications of Raman spectroscopy currently under investigation range from the research laboratory bench-top to the clinical setting at the patients bedside. Raman spectroscopic analysis of biological specimens is advantageous as it provides a spectral fingerprint, rich in molecular compositional information without disrupting the biological environment allowing in-situ biochemical observations to be made. The information dense spectra generate vast sets of complex data in which subtle variations may provide critical clues in data interpretation. Thus the investigation and implementation of advanced data mining and optimization techniques is imperative for complete, rapid and accurate data extraction. Clinical applications of Raman spectroscopy are on the horizon as optical technology progresses, but to fulfill this realization of a new class of biomedical instrumentation, the development of an optimized, fully integrated data processing methodology will be required. In this paper, we describe several methods for pre-processing raw Raman Spectra, followed by data mining techniques used for classifying spectra of cancerous cells based on cell type, environments and mechanisms of cell death.