Gene selection and classification of microarray data using random forest

@article{DazUriarte2005GeneSA,
  title={Gene selection and classification of microarray data using random forest},
  author={Ram{\'o}n D{\'i}az-Uriarte and Sara Alvarez de Andr{\'e}s},
  journal={BMC Bioinformatics},
  year={2005},
  volume={7},
  pages={3 - 3}
}
Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use… CONTINUE READING
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