Detection of Novel Biomarker Genes of Alzheimer’s Disease Using Gene Expression Data
S. P. C. Perera, K. Pradeep Hewage, Chamara Gunarathne, Rajitha Navarathna, Damayanthi Herath, Roshan Ragel
Abstract
It is well recognized, that most common form of dementia is Alzheimer's disease and a successful cure or medication is not discovered. A plethora of research has been conducted to understand the underlying mechanism and the pathogenesis of the Alzheimer's disease. To explore the underlying genetic structure of the disease, gene expression data is being used by many researches and computational and statistical approaches were used to identify possible genes that are risk. In this paper, we propose a machine learning framework that can be used to identify possible bio-marker genes. Our experiments discover possible set of 14 genes, which some of them are validated by biological sources. We also present a critical analysis of the propose machine learning framework using GSE5281 gene dataset.