Random Forest as a Tumour Genetic Marker Extractor
This work addresses cancer detection and therapy development for biomedical applications, but it is incremental as it applies an existing method to new data.
The paper tackled the problem of identifying tumour genetic markers from chromosome rearrangements in 2,586 cancer patients, using a Random Forest classifier to evaluate feature relevance and resulting in a set of potential markers, some validated and others novel.
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.