This thesis aims to investigate the integration between a specific category of biomedical images, the histological ones, and DNA methylation. I consider colon cancer data derived from The Cancer Genome Atlas (TCGA) repository. Concerning images, I also exploit an additional set of Regions Of Interest (ROIs). To achieve the aim, I train an image classification model to predict the malignancy in the images. Afterward, I analyze how methylation affects the predictions by exploiting the correlation between the features extracted from the two data types. The input data consists of the methylation samples, divided between healthy and tumor, and the images, which are also globally labeled as tumor or healthy.