Predicting the clinical status of human breast cancer by using gene expression profiles

By M. West, Carrie Blanchette, H. Dressman, Erich Huang, Seiichi Ishida, Rainer Spang, H. Zuzan, J. Olson, J. Marks, Joseph R. Nevins. Proceedings of the National Academy of Sciences of the United States of America, 2001

1,465 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/da0708923ea733f7b1fb25aa8971df37080d986f

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Citations in the corpus

Accurate Cancer Classification Using Expressions of Very Few Genes (2007, methodology): “For a lymphoma patient: In recent years, the method of honestly evaluating classifiers in the context of gene expression-based cancer classification has been discussed in the literature [10], [19], [20], [21], [22].”
Gene expression Detecting potential labeling errors in microarrays by data perturbation (2006, background): “For example, West et al. (2001) analyzed 49 breast tumor samples and identified 9 samples as possibly having the wrong labels.”
GATA-3 maintains the differentiation of the luminal cell fate in the mammary gland. (2006, neutral): “ER� human breast tumors (Bertucci et al., 2000; Farmer et al., 2005; Gruvberger et al., 2001; Hoch et al., 1999; Mehra et al., 2005; Perou et al., 2000; Sorlie et al., 2003; Sotiriou et al., 2003; van ‘t Veer et al., 2002; Wang et al., 2005; West et al., 2001 ).”
Dimensionality Reduction via Matrix Factorization for Predictive Modeling from Large, Sparse Behavioral Data (2015, methodology): “2011), (West et al. 2001), and (Burl et al.”
A simple and efficient algorithm for gene selection using sparse logistic regression (2003, positive): “We also evaluated our algorithm on the breast cancer data set (West et al., 2001).”
Correcting Sample Selection Bias by Unlabeled Data (2006, methodology): “We study a very similar scenario on two breast cancer microar ray datasets from [4] and [19], measuring the expression levels of 2,166 common genes for norma l and cancer patients [18].”
Clustering cancer gene expression data: a comparative study (2008, neutral): “Armstrong-V1 [52] Affy Blood 72 2 24,48 12582 1081 Armstrong-V2 [52] Affy Blood 72 3 24,20,28 12582 2194 Bhattacharjee [9] Affy Lung 203 5 139,17,6,21,20 12600 1543 Chowdary [13] Affy Breast, Colon 104 2 62,42 22283 182 Dyrskjot [14] Affy Bladder 40 3 9,20,11 7129 1203 Golub-V1 [3] Affy Bone marrow ”
Classifying Gene Expression Profiles from Pairwise mRNA Comparisons (2004, neutral): “• Breast: Determining Lymph Node Status in Breast Tumor Samples: The data (West et al. (2001)) consist of gene expression profiles measured in breast tumor samples.”
A review of microarray datasets and applied feature selection methods (2014, neutral): “Breast Cancer 97 24,481 unknown [118] 2002 [24,23,27] [8] Breast-test 19 24,481 37–63% [118] 2002 [23,48] [8] Breast-train 78 24,481 56–44% [118] 2002 [23,48] [8] BreastER 49 7129 49–51% [123] 2001 [101] unknown”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “[96] Margin influence analysis (MIA) & Colon [64]; SVM LOOCV & Breast [97];”
Feature Selection via Dependence Maximization (2012, background): “, 2001) (7, 8), two with breast cancer (Gruvberger et al., 2001; West et al., 2001) (9, 10), and two with leukaemia (Bullinger et al.”
A Practical Approach to Microarray Data Analysis (2003, background): “The issue of “honest” crossvalidation analysis is discussed in (West et al., 2001).”