RankGene: identification of diagnostic genes based on expression data. Summary: RankGene is a program for analyzing gene expression data and computing diagnostic genes based on their predictive power in distinguishing between different types of samples. The program integrates into one system a variety of popular ranking criteria, ranging from the traditional t-statistic to one-dimensional support vector machines. This flexibility makes RankGene a useful tool in gene expression analysis and feature selection. Availability: http://genomics10.bu.edu/yangsu/rankgene

References in zbMATH (referenced in 15 articles )

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  1. Bastien, B.; Boukhobza, T.; Dumond, H.; Gégout-Petit, A.; Muller-Gueudin, A.; Thiébaut, C.: A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology (2022)
  2. Wang, Chamont; Gevertz, Jana L.: Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches (2016)
  3. Zhao, Yuhai; Li, Yuan; Yin, Ying; Sheng, Gang: Finding top-(k) covering irreducible contrast sequence rules for disease diagnosis (2015)
  4. Archetti, Francesco; Giordani, Ilaria; Vanneschi, Leonardo: Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset (2010)
  5. Bar, Haim; Booth, James; Schifano, Elizabeth; Wells, Martin T.: Laplace approximated EM microarray analysis: an empirical Bayes approach for comparative microarray experiments (2010)
  6. Kung, S. Y.; Luo, Yuhui; Mak, Man-Wai: Feature selection for genomic signal processing: unsupervised, supervised, and self-supervised scenarios (2010) ioport
  7. Luss, Ronny; d’Aspremont, Alexandre: Clustering and feature selection using sparse principal component analysis (2010)
  8. Yang, Pengyi; Zhou, Bing Bing; Zhang, Zili; Zomaya, Albert Y.: A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data (2010) ioport
  9. Hua, Jianping; Tembe, Waibhav D.; Dougherty, Edward R.: Performance of feature-selection methods in the classification of high-dimension data (2009)
  10. Moretti, Stefano: Game theory applied to gene expression analysis (2009)
  11. d’Aspremont, Alexandre; Bach, Francis; El Ghaoui, Laurent: Optimal solutions for sparse principal component analysis (2008)
  12. Fragnelli, Vito; Moretti, Stefano: A game theoretical approach to the classification problem in gene expression data analysis (2008)
  13. Yeh, Jinn-Yi: Applying data mining techniques for cancer classification on gene expression data (2008)
  14. Moretti, Stefano; Patrone, Fioravante; Bonassi, Stefano: The class of microarray games and the relevance index for genes (2007)
  15. Wang, Yu.; Tetko, Igor V.; Hall, Mark A.; Frank, Eibe; Facius, Axel; Mayer, Klaus F. X.; Mewes, Hans W.: Gene selection from microarray data for cancer classification -- a machine learning approach (2005)