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 77 articles )

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  1. Loecher, Markus: Unbiased variable importance for random forests (2022)
  2. Loynes, Christopher; Ouenniche, Jamal; De Smedt, Johannes: The detection and location estimation of disasters using Twitter and the identification of non-governmental organisations using crowdsourcing (2022)
  3. Friedberg, Rina; Tibshirani, Julie; Athey, Susan; Wager, Stefan: Local linear forests (2021)
  4. Guo, Liang; Liu, Jianya; Lu, Ruodan: Subsampling bias and the best-discrepancy systematic cross validation (2021)
  5. Hooker, Giles; Mentch, Lucas; Zhou, Siyu: Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance (2021)
  6. Makariou, Despoina; Barrieu, Pauline; Chen, Yining: A random forest based approach for predicting spreads in the primary catastrophe bond market (2021)
  7. Salles, Thiago; Rocha, Leonardo; Gonçalves, Marcos: A bias-variance analysis of state-of-the-art random forest text classifiers (2021)
  8. Wang, Hui; Wang, Guizhi: Improving random forest algorithm by Lasso method (2021)
  9. Davis, Richard A.; Nielsen, Mikkel S.: Modeling of time series using random forests: theoretical developments (2020)
  10. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  11. Lopes, Miles E.; Wu, Suofei; Lee, Thomas C. M.: Measuring the algorithmic convergence of randomized ensembles: the regression setting (2020)
  12. Mišić, Velibor V.: Optimization of tree ensembles (2020)
  13. Zhong, Guo; Pun, Chi-Man: Nonnegative self-representation with a fixed rank constraint for subspace clustering (2020)
  14. Ayyad, Sarah M.; Saleh, Ahmed I.; Labib, Labib M.: A new distributed feature selection technique for classifying gene expression data (2019)
  15. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  16. 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)
  17. Jain, Yashita; Ding, Shanshan; Qiu, Jing: Sliced inverse regression for integrative multi-omics data analysis (2019)
  18. Kim, Ilmun; Lee, Ann B.; Lei, Jing: Global and local two-sample tests via regression (2019)
  19. Wang, Ling; Zhou, Dongfang; Tian, Hui; Zhang, Hao; Zhang, Wei: Parametric fault diagnosis of analog circuits based on a semi-supervised algorithm (2019)
  20. 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)

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