Improving the understanding of cancer in a descriptive way: An emerging pattern mining-based approach

Abstract

This paper presents an approach based on emerging pattern mining to analyse cancer through genomic data. Unlike existing approaches, mainly focused on predictive purposes, the proposal aims to improve the understanding of cancer descriptively, not requiring either any prior knowledge or hypothesis to be validated. Additionally, it enables to consider high-order relationships, so not only essential genes related to the disease are considered, but also the combined effect of various secondary genes that can influence different pathways directly or indirectly related to the disease. The prime hypothesis is that splitting genomic cancer data into two subsets, that is, cases and controls, will allow us to determine which genes, and their expressions, are associated with different cancer types. The possibilities of the proposal are demonstrated by analyzing RNA-Seq data for six different types of cancer: breast, colon, lung, thyroid, prostate, and kidney. Some of the extracted insights were already described in the related literature as good cancer bio-markers, while others have not been described yet mainly due to existing techniques are biased by prior knowledge provided by biological databases.