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.
