A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer - Tabl, Ashraf Abou; Alkhateeb, Abedalrhman; ElMaraghy, Waguih; Rueda, Luis; Ngom, Alioune (Frontiers in Genetics, 2019)
Studying breast cancer survivability among different patients who received various treatment therapies may help us understand the relationship between the survivability and treatment of patients based on genetic expression. In this work, we present a classification system that predicts whether a given breast cancer patient who underwent through hormone therapy, radiotherapy, or surgery will survive beyond five years after treatment. Our classifier is a tree-based hierarchical approach that groups breast cancer patients based on survivability classes. Each node in the tree is associated with a treatment therapy and a subset of genes that can best predict whether a given patient will survive for more than 5 years after that particular treatment. We applied our tree-based method to a gene expression dataset about 347 treated breast cancer patients and identified potential subsets of biomarkers that can predict survivability with high accuracy levels, ranging from 80.9% to 100%. We investigated the roles of many biomarkers through a literature review and found that certain biomarkers are strongly related to breast cancer survivability.