Gene Expression Patterns and Profile Changes Pre- and Post-Erlotinib Treatment in Patients with Metastatic Breast Cancer

2005 ◽  
Vol 11 (17) ◽  
pp. 6226-6232 ◽  
Author(s):  
Sherry X. Yang ◽  
Richard M. Simon ◽  
Antoinette R. Tan ◽  
Diana Nguyen ◽  
Sandra M. Swain
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Metastatic Breast Cancer ◽  
Expression Patterns ◽  
Metastatic Breast ◽  
Gene Expression Patterns ◽  
Profile Changes

Predicting features of breast cancer with gene expression patterns

2007 ◽  
Vol 108 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Xuesong Lu ◽  
Xin Lu ◽  
Zhigang C. Wang ◽  
J. Dirk Iglehart ◽  
Xuegong Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns

scholarly journals SIGN: similarity identification in gene expression

2019 ◽  
Vol 35 (22) ◽  
pp. 4830-4833 ◽  
Author(s):  
Seyed Ali Madani Tonekaboni ◽  
Venkata Satya Kumar Manem ◽  
Nehme El-Hachem ◽  
Benjamin Haibe-Kains
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Open Source ◽  
Supervised Learning ◽  
Cancer Patients ◽  
Biomedical Research ◽  
Expression Patterns ◽  
R Package ◽  
Gene Expression Patterns ◽  
Learning Schemes

Abstract Motivation High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. Results We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community. Availability and implementation An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).

Abstract 1438: Influence of the neural environment on the gene expression profile of metastatic breast cancer

2010 ◽  
Author(s):  
Jacob E. Shabason ◽  
Tamalee Scott ◽  
Shuping Zhao ◽  
Uma T. Shankavaram ◽  
Philip J. Tofilon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Metastatic Breast Cancer ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Metastatic Breast
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