Extracting Conditions Specific Key Genes from Basal-Like Breast Cancer Gene Expression Data using Gene Co-expression Network

2018 ◽  
Author(s):  
Ankush Maind ◽  
Shital Rant
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Cancer Gene ◽  
Expression Data ◽  
Basal Like Breast Cancer ◽  
Key Genes ◽  
Breast Cancer Gene

scholarly journals Logic-based analysis of gene expression data predicts association between TNF, TGFB1 and EGF pathways in basal-like breast cancer

Methods ◽  
2020 ◽  
Vol 179 ◽  
pp. 89-100 ◽  
Author(s):  
Kyuri Jo ◽  
Beatriz Santos-Buitrago ◽  
Minsu Kim ◽  
Sungmin Rhee ◽  
Carolyn Talcott ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Basal Like Breast Cancer

scholarly journals Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

2003 ◽  
Vol 19 (9) ◽  
pp. 1079-1089 ◽  
Author(s):  
G. Getz ◽  
H. Gal ◽  
I. Kela ◽  
D. A. Notterman ◽  
E. Domany
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Colon Cancer ◽  
Gene Expression Data ◽  
Clustering Analysis ◽  
Cancer Gene ◽  
Expression Data

Joint reconstruction of multiple gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity

2020 ◽  
Vol 36 (9) ◽  
pp. 2755-2762
Author(s):  
Jia-Juan Tu ◽  
Le Ou-Yang ◽  
Hong Yan ◽  
Xiao-Fei Zhang ◽  
Hong Qin
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Tumor Heterogeneity ◽  
Supplementary Information ◽  
Cancer Type ◽  
Cancer Gene ◽  
Expression Data ◽  
Cancer Subtypes

Abstract Motivation Reconstruction of cancer gene networks from gene expression data is important for understanding the mechanisms underlying human cancer. Due to heterogeneity, the tumor tissue samples for a single cancer type can be divided into multiple distinct subtypes (inter-tumor heterogeneity) and are composed of non-cancerous and cancerous cells (intra-tumor heterogeneity). If tumor heterogeneity is ignored when inferring gene networks, the edges specific to individual cancer subtypes and cell types cannot be characterized. However, most existing network reconstruction methods do not simultaneously take inter-tumor and intra-tumor heterogeneity into account. Results In this article, we propose a new Gaussian graphical model-based method for jointly estimating multiple cancer gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity. Given gene expression data of heterogeneous samples for different cancer subtypes, a non-cancerous network shared across different cancer subtypes and multiple subtype-specific cancerous networks are estimated jointly. Tumor heterogeneity can be revealed by the difference in the estimated networks. The performance of our method is first evaluated using simulated data, and the results indicate that our method outperforms other state-of-the-art methods. We also apply our method to The Cancer Genome Atlas breast cancer data to reconstruct non-cancerous and subtype-specific cancerous gene networks. Hub nodes in the networks estimated by our method perform important biological functions associated with breast cancer development and subtype classification. Availability and implementation The source code is available at https://github.com/Zhangxf-ccnu/NETI2. Supplementary information Supplementary data are available at Bioinformatics online.

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