Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer
Author:
Affiliation:
1. African Institute for Mathematical Sciences, Muizenberg, South Africa
2. Massachusetts College of Pharmacy and Health Sciences, Pharmaceutical Sciences, Worcester, Massachusetts, United States
Funder
Resources of Ghana Institute of Management and Public Administration, Ghana
African Institute for Mathematical Sciences
MCPHS University, USA
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Link
http://academic.oup.com/bib/article-pdf/20/2/504/28834006/bbx138.pdf
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3. Evaluation of HER-2/neu gene amplification and overexpression: comparison of frequently used assay methods in a molecularly characterized cohort of breast cancer specimens;Press;J Clin Oncol,2002
4. Estrogen-receptor, progesterone-receptor and HER2 status determination in invasive breast cancer. concordance between immuno-histochemistry and MapQuantTM microarray based assay;Mouttet;PLoS One,2016
5. Concordance between central and local laboratory HER2 testing from a community-based clinical study;Reddy;Clin Breast Cancer,2006
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