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 64 articles )

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  1. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  2. Ayyad, Sarah M.; Saleh, Ahmed I.; Labib, Labib M.: A new distributed feature selection technique for classifying gene expression data (2019)
  3. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  4. El Haouij, Neska; Poggi, Jean-Michel; Ghozi, Raja; Sevestre-Ghalila, Sylvie; Jaïdane, Mériem: Random forest-based approach for physiological functional variable selection for driver’s stress level classification (2019)
  5. Jain, Yashita; Ding, Shanshan; Qiu, Jing: Sliced inverse regression for integrative multi-omics data analysis (2019)
  6. Kim, Ilmun; Lee, Ann B.; Lei, Jing: Global and local two-sample tests via regression (2019)
  7. Wang, Ling; Zhou, Dongfang; Tian, Hui; Zhang, Hao; Zhang, Wei: Parametric fault diagnosis of analog circuits based on a semi-supervised algorithm (2019)
  8. Yu, Xinghao; Xiao, Lishun; Zeng, Ping; Huang, Shuiping: Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information (2019)
  9. Duroux, Roxane; Scornet, Erwan: Impact of subsampling and tree depth on random forests (2018)
  10. Gilles Kratzer, Reinhard Furrer: varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets (2018) arXiv
  11. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  12. Janitza, Silke; Celik, Ender; Boulesteix, Anne-Laure: A computationally fast variable importance test for random forests for high-dimensional data (2018)
  13. Gregorutti, Baptiste; Michel, Bertrand; Saint-Pierre, Philippe: Correlation and variable importance in random forests (2017)
  14. Tyralis, Hristos; Papacharalampous, Georgia: Variable selection in time series forecasting using random forests (2017)
  15. Biau, Gérard; Scornet, Erwan: A random forest guided tour (2016)
  16. De Niz, Carlos; Rahman, Raziur; Zhao, Xiangyuan; Pal, Ranadip: Algorithms for drug sensitivity prediction (2016)
  17. Scornet, Erwan: On the asymptotics of random forests (2016)
  18. 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)
  19. Booth, Ash; Gerding, Enrico; McGroarty, Frank: Performance-weighted ensembles of random forests for predicting price impact (2015)
  20. Gregorutti, Baptiste; Michel, Bertrand; Saint-Pierre, Philippe: Grouped variable importance with random forests and application to multiple functional data analysis (2015)

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