Nonnegative Matrix Factorization for Dynamic Modules in Cancer Attribute Temporal Networks Tracking the dynamic modules of genome associated with cancer progression is essential for studying cancer pathogenesis, diagnosis and treatment. The current algorithms only focus on extracting dynamic modules from temporal networks associated with cancer progression, where networks are homogeneous. However, as far as we know, there is no attempt has been made to extract dynamic modules through fusing gene attribute information with gene coexpression temporal networks. To solve this problem, an novel algorithm (aka TANMF) is proposed to detect dynamic epigenetic modules in cancer attribute networks, which is formulated as an optimization problem. Specifically, we decompose the gene attribute matrix and gene coexpression temporal networks under the same basis matrix at each stage, and sparse the feature matrix by  L_1  regularization to increase the robustness of the algorithm. Experimental results demonstrate that the proposed algorithm is more accurate than stateoftheart methods on simulated data. By applying TANMF to breast cancer data, the obtained dynamic modules are more enriched by the known pathways. The proposed model and algorithm provide an effective way to introduce gene attribute to investigate dynamics of cancer.

Identifying Structural Changes in Correlation Networks Models of Cancer Gene Expression by Stage Gene expression analysis using correlation network modeling can help to identify systemslevel cellular changes and cooperation among genes. Network modeling is a relatively novel method for comparing changes across stages of cancer, or between primary tumor tissue and the metastatic tissue. In this study, we develop a pipeline to identify the dynamic changes of cancer in gene expression level through timedependent network analysis of cancer data from The Cancer Genome Atlas (TCGA). A total of 16 correlation networks were built from four (4) stages in four (4) different types of cancers: Thyroid Carcinoma, Colon Adenocarcinoma and Rectum Adenocarcinoma, Stomach Adenocarcinoma, and Kidney Renal Clear Cell Carcinoma. To identify the basic changes in network structure, we performed Jaccard similarity comparison of structurally relevant nodes. We employed mutation analysis to measure and present the timebased changes in mutation rate of genes that are specific to each cancer type. Finally, we present a case study to identify the gene expression changes among primary tumor tissue and metastatic tissue in skin cutaneous melanoma (SKCM).