RankGene

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 12 articles )

Showing results 1 to 12 of 12.
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  1. Wang, Chamont; Gevertz, Jana L.: Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches (2016)
  2. Zhao, Yuhai; Li, Yuan; Yin, Ying; Sheng, Gang: Finding top-$k$ covering irreducible contrast sequence rules for disease diagnosis (2015)
  3. Archetti, Francesco; Giordani, Ilaria; Vanneschi, Leonardo: Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset (2010)
  4. Kung, S.Y.; Luo, Yuhui; Mak, Man-Wai: Feature selection for genomic signal processing: unsupervised, supervised, and self-supervised scenarios (2010)
  5. Luss, Ronny; d’Aspremont, Alexandre: Clustering and feature selection using sparse principal component analysis (2010)
  6. Hua, Jianping; Tembe, Waibhav D.; Dougherty, Edward R.: Performance of feature-selection methods in the classification of high-dimension data (2009)
  7. Moretti, Stefano: Game theory applied to gene expression analysis (2009)
  8. d’Aspremont, Alexandre; Bach, Francis; El Ghaoui, Laurent: Optimal solutions for sparse principal component analysis (2008)
  9. Fragnelli, Vito; Moretti, Stefano: A game theoretical approach to the classification problem in gene expression data analysis (2008)
  10. Yeh, Jinn-Yi: Applying data mining techniques for cancer classification on gene expression data (2008)
  11. Moretti, Stefano; Patrone, Fioravante; Bonassi, Stefano: The class of microarray games and the relevance index for genes (2007)
  12. Wang, Yu.; Tetko, Igor V.; Hall, Mark A.; Eibe, Frank; Facius, Axel; Mayer, Klaus F.X.; Mewes, Hans W.: Gene selection from microarray data for cancer classification -- a machine learning approach (2005)