A novel approach for finding informative genes in ten subtypes of breast cancer

World wide, one in nine women are diagnosed with breast cancer in their lifetime and breast cancer is the second leading cause of death among women. Accurate diagnosis of the specific subtypes of this disease is vital to ensure that patients will have the best possible response to therapy. One way to discriminate subtypes of breast cancer is to study those genes that differentially express across different subtypes. In this study, we use different machine learning techniques to select the most informative genes corresponding to ten subtypes of breast cancer. In particular, we propose a new bottom-up hierarchical classification approach to select the most informative genes for different subtypes, while we identify the similarity level between these subtypes. Our results support that this new approach to gene selection yields a small subset of genes that can predict each of these ten subtypes with very high accuracy. Moreover, the proposed model provides an insightful structure for further analysis of these subtypes.