Random Forest as a Tumour Genetic Marker Extractor

TL;DR

This study uses a Random Forest classifier on a large cancer dataset to identify and validate potential tumour genetic markers, aiding cancer detection and therapy development.

Contribution

It introduces a novel application of Random Forests for extracting tumour genetic markers from chromosome rearrangement data.

Findings

Identification of potential genetic markers validated by literature

Discovery of novel potential tumour markers

Effective feature evaluation using Random Forests

Abstract

Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development. In this paper, we explore a recently released dataset of chromosome rearrangements in 2,586 cancer patients, where different sorts of alterations have been detected. Using a Random Forest classifier, we evaluate the relevance of several features (some directly available in the original data, some engineered by us) related to chromosome rearrangements. This evaluation results in a set of potential tumour genetic markers, some of which are validated in the bibliography, while others are potentially novel.