Convolutional Neural Networks In Classifying Cancer Through DNA Methylation
This work addresses the need for early diagnosis of cancer types based on epigenetic data, but it appears incremental as it applies an existing CNN method to a specific domain without novel methodological breakthroughs.
The authors tackled the problem of classifying cancer types using DNA methylation patterns by proposing a Convolutional Neural Network (CNN) model, which learned from publicly available datasets to classify new DNA methylation profiles.
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.