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 44 articles )

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  1. Gilles Kratzer, Reinhard Furrer: varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets (2018) arXiv
  2. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  3. Janitza, Silke; Celik, Ender; Boulesteix, Anne-Laure: A computationally fast variable importance test for random forests for high-dimensional data (2018)
  4. Gregorutti, Baptiste; Michel, Bertrand; Saint-Pierre, Philippe: Correlation and variable importance in random forests (2017)
  5. Biau, Gérard; Scornet, Erwan: A random forest guided tour (2016)
  6. Scornet, Erwan: On the asymptotics of random forests (2016)
  7. 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)
  8. Gregorutti, Baptiste; Michel, Bertrand; Saint-Pierre, Philippe: Grouped variable importance with random forests and application to multiple functional data analysis (2015)
  9. Scornet, Erwan; Biau, Gérard; Vert, Jean-Philippe: Consistency of random forests (2015)
  10. Tsai, Miao-Yu: Variable selection in Bayesian generalized linear-mixed models: an illustration using candidate gene case-control association studies (2015)
  11. Yang, Aijun; Li, Yunxian; Tang, Niansheng; Lin, Jinguan: Bayesian variable selection in multinomial probit model for classifying high-dimensional data (2015)
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  18. Kruppa, Jochen; Liu, Yufeng; Diener, Hans-Christian; Holste, Theresa; Weimar, Christian; König, Inke R.; Ziegler, Andreas: Probability estimation with machine learning methods for dichotomous and multicategory outcome: applications (2014)
  19. Pappu, Vijay; Pardalos, Panos M.: High-dimensional data classification (2014)
  20. Williams, John K.: Using random forests to diagnose aviation turbulence (2014) ioport

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