article Open AccessTop 1% cited
Gene selection and classification of microarray data using random forest
BMC Bioinformatics · 2006 · Vol. 7(1) · pp. 3–3
Ramón Díaz‐Uriarte✉(Spanish National Cancer Research Centre)Sara Álvarez de Andrés(Spanish National Cancer Research Centre)
Abstract
Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.
Gene expression and cancer classificationEvolutionary Algorithms and ApplicationsFace and Expression RecognitionRandom forestComputer scienceSupport vector machineSelection (genetic algorithm)UnivariateData miningRanking (information retrieval)DNA microarrayGene selectionFeature selection
MeSH terms
AlgorithmsComputer SimulationModels, GeneticPattern Recognition, AutomatedModels, StatisticalCluster AnalysisOligonucleotide Array Sequence AnalysisGene Expression Profiling
Funding
- Ministerio de Economía y Competitividad
Citations
2,930
FWCI
29.05
field-weighted impact
References
73
Percentile
100%
vs. same field & year
Citations per year
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