A novel aggregate gene selection method for microarray data classification
- 1 August 2015
- journal article
- Published by Elsevier BV in Pattern Recognition Letters
- Vol. 60-61, 16-23
- https://doi.org/10.1016/j.patrec.2015.03.018
Abstract
No abstract availableKeywords
Funding Information
- Australian Research Council (DP120102112)
- Deakin University
This publication has 22 references indexed in Scilit:
- A Novel Strategy for Gene Selection of Microarray Data Based on Gene-to-Class Sensitivity InformationPLOS ONE, 2014
- Fold change rank ordering statistics: a new method for detecting differentially expressed genesBMC Bioinformatics, 2014
- Evaluating methods for ranking differentially expressed genes applied to microArray quality control dataBMC Bioinformatics, 2011
- Gene boosting for cancer classification based on gene expression profilesPattern Recognition, 2009
- Comparison and evaluation of methods for generating differentially expressed gene lists from microarray dataBMC Bioinformatics, 2006
- Gene selection and classification of microarray data using random forestBMC Bioinformatics, 2006
- MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATAJournal of Bioinformatics and Computational Biology, 2005
- Feature extraction based on the Bhattacharyya distancePattern Recognition, 2003
- Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression MonitoringScience, 1999
- Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arraysProceedings of the National Academy of Sciences of the United States of America, 1999