title: Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using sparse logistic regression.
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.
authors: Department of Computer Science, Houghton College, Houghton 14744, NY, USA. wei.hu@houghton.edu
authors: Wei
authors: W
authors: Hu
mesh_terms: D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000473:pathology; D019295:Computational Biology; D005260:Female; D006801:Humans; D016015:Logistic Models; D020360:Neoadjuvant Therapy / Q000379:methods*; D018365:Neoplasm, Residual / Q000188:drug therapy / Q000473:pathology; D051880:Receptors, Notch / Q000235:genetics* / Q000378:metabolism; D015398:Signal Transduction
chemical_list: D051880:Receptors, Notch