GeneSrF

GeneSrF: gene selection with random forests (v. 20070524). GeneSrF is a web tool for gene selection in classification problems that uses random forest. Two approaches for gene selection are used: one is targeted towards identifying small, non-redundant sets of genes that have good predictive performance. The second is a more heuristic graphical approach that can be used to identify large sets of genes (including redundant genes) related to the outcome of interest. The first approach is described in detail in this paper. The R code is available as an R package from CRAN or from this link. For further details see the help.


References in zbMATH (referenced in 30 articles )

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  1. Gregorutti, Baptiste; Michel, Bertrand; Saint-Pierre, Philippe: Correlation and variable importance in random forests (2017)
  2. Biau, Gérard; Scornet, Erwan: A random forest guided tour (2016)
  3. Scornet, Erwan: On the asymptotics of random forests (2016)
  4. Xi, Maolong; Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun: Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine (2016)
  5. Yang, Aijun; Li, Yunxian; Tang, Niansheng; Lin, Jinguan: Bayesian variable selection in multinomial probit model for classifying high-dimensional data (2015)
  6. Baraud, Yannick; Giraud, Christophe; Huet, Sylvie: Estimator selection in the Gaussian setting (2014)
  7. Bleich, Justin; Kapelner, Adam; George, Edward I.; Jensen, Shane T.: Variable selection for BART: an application to gene regulation (2014)
  8. Cadenas, J.M.; Garrido, M.C.; Martínez, R.: Selecting features from low quality datasets by a fuzzy ensemble (2014) ioport
  9. Hapfelmeier, Alexander; Hothorn, Torsten; Ulm, Kurt; Strobl, Carolin: A new variable importance measure for random forests with missing data (2014)
  10. Pappu, Vijay; Pardalos, Panos M.: High-dimensional data classification (2014)
  11. Williams, John K.: Using random forests to diagnose aviation turbulence (2014) ioport
  12. Yeh, Ching-Chiang; Chi, Der-Jang; Lin, Yi-Rong: Going-concern prediction using hybrid random forests and rough set approach (2014) ioport
  13. Deng, Houtao; Runger, George: Gene selection with guided regularized random forest (2013) ioport
  14. Peng, Xinjun; Xu, Dong: A local information-based feature-selection algorithm for data regression (2013) ioport
  15. Telaar, Anna; Repsilber, Dirk; Nürnberg, Gerd: Biomarker discovery: classification using pooled samples (2013)
  16. Hanczar, Blaise; Nadif, Mohamed: Ensemble methods for biclustering tasks (2012) ioport
  17. Tsai, Chen-An; Huang, Chien-Hsun; Chang, Ching-Wei; Chen, Chun-Houh: Recursive feature selection with significant variables of support vectors (2012)
  18. Mendez, Guillermo; Lohr, Sharon: Estimating residual variance in random forest regression (2011)
  19. Verikas, A.; Gelzinis, A.; Bacauskiene, M.: Mining data with random forests: A survey and results of new tests (2011) ioport
  20. Bühlmann, Peter: Remembrance of Leo Breiman (2010)

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