Expression Data

2004

Citations in the corpus

• Best Subset Selection via a Modern Optimization Lens (2015, methodology): “In addition to the above, we also considered a real microarray dataset: the Leukemia data [Dettling (2004)] downloaded from http://stat.ethz.ch/~dettling/ bagboost.html, with n = 72 binary responses and more than 3000 predictors.”
• Regularization Paths for Generalized Linear Models via Coordinate Descent (2010, methodology): “We used the preprocessed data of Dettling [2004]. • Internet-Ad [Kushmerick, 1999]: document classification problem with mostly binary features.”
• Gene selection and classification of microarray data using random forest (2006, neutral): “This and other papers [7,27,31,32,48,49] (see also above) show that very different classifiers often lead to comparable and successful error rates with a variety of microarray data sets.”
• A Review of Ensemble Methods in Bioinformatics (2010, methodology): “This idea was first investigated by Dettling [87] who proposed to combine the bagging and boosting algorithms (called BagBoosting) for microarray data classification.”
• Smart Regularization of Deep Architectures (2015, methodology): “We also experimented on the preprocessed gene expression datasets 5 that were used in [36].”
• Simple decision rules for classifying human cancers from gene expression profiles (2005, unknown): “Random Forests (Amit and Geman, 1997; Breiman, 2001), bagging (Breiman, 1996) and boosting (Freund and Schapire, 1996, 1997) represent recent success stories of ensemble methods, and all have been shown to perform well in classifying different microarray datasets (Dudoit et al., 2002; Dettling and ”
• The Partial Linear Model in High Dimensions (2013, methodology): “Leukemia, also used in Dettling (2004) and Bühlmann & Mandozzi (2013).”
• Robust biomarker identification for cancer diagnosis with ensemble feature selection methods (2010, methodology): “Feature selection techniques can be broadly characterized into three classes, depending on how they interact with the estimation of the classification model (Saeys et al., 2007).”
• 50 Years of Data Science (2017, methodology): “56 Dettling (2004) developed a framework for comparing classifiers that were common in Machine Learning based on a standard series of datasets (in the 2-class case, the datasets are called ALL, Leukemia, and Prostate, respectively).”
• Boosting Algorithms: Regularization, Prediction and Model Fitting. Rejoinder. (2007, methodology): “We mention here classification of tumor types from gene expressions [25, 26], multivariate financial time series [2, 3, 4], text classification [78], document routing [50] or survival analysis [8] (different from the approach in Section 8).”
• Statistical significance in high-dimensional linear models (2012, methodology): “adaptive Lasso (Zou, 2006), analyzed in the high-dimensional setting by Huang et al. (2008) and van de Geer et al.”
• Applications of Machine Learning in Cancer Prediction and Prognosis (2006, background): “…used in a wide range of applications ranging from detecting and classifying tumors via X-ray and CRT images (Petricoin and Liotta 2004; Bocchi et al. 2004) to the classi fi cation of malignancies from proteomic and genomic (microarray) assays (Zhou et al. 2004; Dettling 2004; Wang et al. 2005).”