Boosting for Tumor Classification with Gene Expression Data

By Marcel Dettling, Peter Buhlmann. Bioinform., 2003

324 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/be454e68b34b9a5ce784e64d420b24c4e454ff9b

How researchers see this paper

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

In Artificial Intelligence

1. Jianqing Fan
2. Yingying Fan
3. Torsten Hothorn
4. Jinchi Lv
5. Peter Buhlmann
6. Adrian E. Raftery
7. Roger E Bumgarner
8. K. Y. Yeung

In Information Systems

1. Jianqing Fan
2. Torsten Hothorn
3. Yingying Fan
4. Adrian E. Raftery
5. K. Y. Yeung
6. Roger E Bumgarner
7. Y. H. Yang
8. Pengyi Yang

In Molecular Biology

1. Jianqing Fan
2. Yingying Fan
3. Adrian E. Raftery
4. Torsten Hothorn
5. K. Y. Yeung
6. Roger E Bumgarner
7. Pengyi Yang
8. Y. H. Yang

Citations in the corpus

• Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data (2005, methodology): “Dudoit et al. (2002) compared the performance of different discrimination methods, including nearest neighbor classifiers, linear discriminant analysis and classification trees, for classifying multiple tumor types using gene-expression data. Recently, Li et al. (2004) studied the performance of var”
• Simple decision rules for classifying human cancers from gene expression profiles (2005, methodology): “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 B”
• Classifying Gene Expression Profiles from Pairwise mRNA Comparisons (2004, unknown): “…routinely applied to microarray data, examples being neural networks (Bicciato et al. (2003); Bloom et al. (2004); Khan et al. (2001)), decision trees (Boulestiex et al. (2003); Dettling and Buhlmann (2003); Zhang et al. (2003)) and support vector machines (Peng et al. (2003); Yeang et al. (2001)).”
• High Dimensional Classification Using Features Annealed Independence Rules. (2007, methodology): “ar X iv :m at h/ 07 01 10 8v 2 [ m at h. ST ] 2 3 Ja n 20 09 The Annals of Statistics 2008, Vol. 36, No. 6, 2605–2637 DOI: 10.1214/07-AOS504 c© Institute of Mathematical Statistics, 2008”
• A Review of Ensemble Methods in Bioinformatics (2010, methodology): “For instances, Dettling and Bühlmann [32] proposed an algorithm called LogitBoost which replaces the exponential loss function used in AdaBoost with a log-likelihood loss function.”
• 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).”