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• W2025703469 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." @default.
• W2025703469 created "2016-06-24" @default.
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• W2025703469 date "2013-01-01" @default.
• W2025703469 modified "2023-10-02" @default.
• W2025703469 title "Predicting Pathologic Complete Response to neoadjuvant chemotherapy in breast cancer using Sparse Logistic Regression" @default.
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• W2025703469 doi "https://doi.org/10.1504/ijbra.2013.053605" @default.
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