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.

Cancer Biomarker Discovery from Gene Coexpression Networks Using Community Detection Methods Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in coexpression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene coexpression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNAseq gene expression data of three cancers  Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM)  from The Cancer Genome Atlas (TCGA) are used to construct gene coexpression networks using Pearson Correlation. Six wellknown community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic covariates. The communities that could distinguish the cancer patients between high and lowrisk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.