Convolutional Neural Networks In Classifying Cancer Through DNA Methylation

TL;DR

This paper explores the use of convolutional neural networks to classify cancer types based on DNA methylation patterns, aiming to improve early diagnosis through deep learning analysis of epigenetic data.

Contribution

It introduces a CNN-based deep learning model that classifies cancer types from DNA methylation profiles, leveraging publicly available datasets for improved diagnostic accuracy.

Findings

CNN model accurately classifies cancer types from methylation data

Deep learning approach outperforms traditional methods

Potential for early cancer detection using methylation patterns

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.