Publications

Abstract : To analyze the clinical trial data corresponding to proneness of Alzheimer's disease, to identify the epigenetic attributes helpful in making the prediction for different ethnic groups and to establish a correlation between the epigenetic factors and ethnicities towards AD responsiveness. The proneness of Alzheimer's disease varies among different ethnicities due to the variations in epigenetic, metagenomic and environmental factors. The epigenetic variants are highly contributing towards individual responsiveness. In the present work, clinical information from four major ethnic groups, African-American, Asian, Caucasian and Latino have been used for the analysis. The predictions have been made through machine learning approach using the kernel magic of 'support vector machine'. The behavior has been compared with the global population to quantify the influence of different ethnic groups. It has been found that the AD proneness can be effectively predicted through the RBF kernel of SVM with specific bias offset parameters. These parameters have been identified as unique computational markers of each ethnic group.