Group classification based on high-dimensional data: application to differential scanning calorimetry plasma thermogram analysis of cervical cancer and control samples

Abstract

Abstract: Differential scanning calorimetry has been applied to identify protein denaturation patterns, or thermograms, in blood plasma samples that are indicative of health status. Data sets generated by differential scanning calorimetry are high dimensional, and it is complex to analyze and classify thermogram patterns. The I-RELIEF method is commonly used for group classifica- tion from high-dimensional data sets, such as gene expression data. We report the development and validation of a new method of data reduction and modeling of high-dimensional data sets. The performance of our method was demonstrated through its application to the analysis of differential scanning calorimetry plasma thermogram data. Our method was found to provide superior classification performance compared with the I-RELIEF method.