Title: Convolutional Neural Networks In Classifying Cancer Through DNA Methylation,
Abstract: DNA Methylation has been the most extensively studied epigenetic mark.
Usually a change in the genotype, DNA sequence, leads to a change in the
phenotype, observable characteristics of the individual. But DNA methylation,
which happens in the context of CpG (cytosine and guanine bases linked by
phosphate backbone) dinucleotides, does not lead to a change in the original
DNA sequence but has the potential to change the phenotype. DNA methylation is
implicated in various biological processes and diseases including cancer. Hence
there is a strong interest in understanding the DNA methylation patterns across
various epigenetic related ailments in order to distinguish and diagnose the
type of disease in its early stages. In this work, the relationship between
methylated versus unmethylated CpG regions and cancer types is explored using
Convolutional Neural Networks (CNNs). A CNN based Deep Learning model that can
classify the cancer of a new DNA methylation profile based on the learning from
publicly available DNA methylation datasets is then proposed.