Comparison of Different Classification Methods for Breast Cancer Subtypes Prediction Breast cancer is one of the most common cancers among women. Due to heterogeneity of cancers, breast cancer is divided into different subtypes. Different subtypes have different molecular genesis, so the corresponding target cells and treatment plans are different. Identifying the correct cancer subtypes is important for cancer diagnosis and prognosis. Breast cancer subtypes can be divided into four types: Basal, Her2, Luminal A, and Luminal B. Many machine learning approaches have been applied to cancer subtypes classification in the past few years, we present a comparison of different classifiers KNearest Neighbor (KNN), Support Vector Machine (SVM), MultiLayer Perception (MLP), and MultiGrained Cascade Forest (gcForest) on The Cancer Genome Atlas (TCGA) databases of breast cancer. As we all know, biological data are highdimensional and have small sample size, so before classification, we use subtype dependent feature selection method to reduce dimensionality of RNASeq gene expression data. Experimental results show that gcForest has a higher accuracy rate for breast cancer subtypes prediction compared with other classifiers.

Investigating a Breast Cancer Gene Expression Data Using a Novel Clustering Approach Historically, breast cancer has been perceived as a disease with varying histological and clinical features. Breast cancer tumor classification is important in disease prognosis and prediction because different breast tumors respond differently to different treatments and have different survival rates. Gene expression profiling studies have increasingly been motivated in the past decades to develop a good classification of breast cancer in molecular subtypes, which can improve the standard clinical assessments by providing extra prognostic information. In this research, one of the most comprehensive breast cancer gene expression datasets is analyzed by applying a novel clustering approach to predict the breast cancer subtypes. The novel unsupervised clustering approach initially model the gene expression data as a network and employ a community detection method to identify network clusters. This method utilizes an efficient problem specific metaheuristic algorithm to optimize the modularity value and identify clusters of breast cancer samples with similar characteristics that presents different subtypes of breast cancer. To assess the significant of the newly defined breast cancer subtypes, we compared our findings with three breast cancer subtyping methods.