Predicting Pathologic Complete Response to neoadjuvant chemotherapy in breast cancer using Sparse Logistic Regression

Authors:
Wei Hu
Affiliations:
Department of Computer Science, Houghton College, Houghton 14744, NY, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2013

Citing 2
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Abstract

We utilised Sparse Logistic Regression SLR to build two sparse and interpretable predictors. The first one SLR-65 was based on a signature consisting of the top 65 probe sets 59 genes differentially expressed between Pathologic Complete Response PCR and Residual Disease RD cases, and the second one SLR-Notch was based on the genes involved in the Notch singling related pathways 113 genes. The two predictors produced better predictions than the predictor in a previous study. The SLR-65 selected 16 informative genes and the SLR-Notch selected 12 informative genes.