Classification of gene microarrays by penalized logistic regression.

By Ji Zhu, Trevor J. Hastie. Biostatistics, 2004

403 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0f33ea53b4c45d4a285bf6a139f0f065aff74724

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jieping Ye
2. Robert Tibshirani
3. Trevor J. Hastie
4. Francis R. Bach
5. Jun Liu
6. Dacheng Tao
7. Luc Van Gool
8. Hui Zou

In Molecular Biology

1. Robert Tibshirani
2. Trevor J. Hastie
3. Jieping Ye
4. Tong Zhang
5. Ji Zhu
6. Hui Zou
7. Jun Liu
8. Jerome H. Friedman

Citations in the corpus

• SMOTE for high-dimensional class-imbalanced data (2013, background): “…and quadratic - DQDA) [27,28], random forests (RF [29]), support vector machine (SVM [30]), prediction analysis for microarrays (PAM [31] also known as nearest shrunken centroids classification) and penalized logistic regression (PLR [32]) with the linear (PLR-L1) and quadratic penalty (PLR-L2).”
• Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, neutral): “, 2005) and classification of transcriptome data (e.g., Tibshirani et al., 2002; Zhu and Hastie, 2004).”
• Penalized logistic regression for detecting gene interactions. (2008, background): “Zhu and Hastie (2004) explored this property of the L2-penalization in (multinomial) penalized LR using continuous factors.”
• An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules (2013, methodology): “Analytical and Experimental Confirmation in CCLs We analyzed the sensitivity data using an orthogonal analytical approach, elastic-net regression (Zhu and Hastie, 2004), which aims to identify a parsimonious model that best predicts response to navitoclax.”
• Regression Shrinkage and Selection via the Elastic Net , with Applications to Microarrays (2003, methodology): “For example, support vector machine and penalized logistic regression (Zhu & Hastie 2003) both use either univariate ranking (UR) (Golub et al. 1999) or recursive feature elimination (RFE) (Guyon et al. 2002) to reduce the number of genes in their final model.”
• Regularization Paths for Generalized Linear Models via Coordinate Descent (2010, background): “As in Zhu and Hastie [2004], here we choose a more symmetric approach.”
• The Entire Regularization Path for the Support Vector Machine (2004, background): “, 2001, Chapter 4] and regularized logistic regression (L)[Zhu and Hastie, 2003], using the same quadratic penalties.”
• Penalized classification using Fisher’s linear discriminant (2011, neutral): “The data set has been studied in a number of papers (see e.g. Zhu and Hastie, 2004; Guo et al., 2007; Witten and Tibshirani, 2009) and is available from http://www-stat.stanford.edu/~hastie/glmnet/glmnetData/ .”
• 1-norm Support Vector Machines (2003, background): “The right panel illustrates the linear relationship between the number of basis functions and the number of joints on the solution path when q < n.”
• Efficient Distributed Learning with Sparsity (2016, methodology): “a `1 regularization, we obtain the `1 regularized logistic regression model (Zhu and Hastie, 2004; Wu et al., 2009):”
• Large-scale sparse logistic regression (2009, background): “ing document classification [4, 5], computer vision [9], natural language processing [14, 22], and bioinformatics [2, 20, 29, 34, 35].”