Multi-group diagnostic classification of high-dimensional data using differential scanning calorimetry plasma thermograms.

ABSTRACT: The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classific

SUBMITTER: Rai SN 

PROVIDER: S-EPMC6701772 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature