We are analyzing https://link.springer.com/article/10.1186/1471-2105-7-3.

Title:
Gene selection and classification of microarray data using random forest | BMC Bioinformatics
Description:
Background 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 gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. Results We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. Conclusion 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.
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Keywords {🔍}

genes, data, selection, error, gene, random, variable, google, scholar, forest, article, classification, number, pubmed, microarray, sets, results, methods, rate, method, cas, set, expression, selected, bootstrap, performance, rates, prediction, samples, procedure, analysis, cancer, ntree, additional, bioinformatics, file, approach, parameters, algorithm, problems, mtry, class, sample, approaches, variables, based, simulated, table, oob, predictive,

Topics {✒️}

org/rdiaz/papers/rfvs/randomforestvarsel alvarez de andrés ramón díaz-uriarte article díaz-uriarte open access article modeling structure-activity relationships org/src/contrib/packages diagonal variance-covariance matrix article download pdf support vector machine exhaustive search method seldi-tof proteomics study cross-validated error rate discover therapeutic targets microarray gene-expression data support vector machines brcal/brca2 tumors gene expression profiling project home page privacy choices/manage cookies including multi-class problems de rijn mv brca1 promoter hypermethylation van der kooy classifying expression data gene expression monitoring gene expression revealed gene expression correlates colon data sets primary solid tumors clinical drug efficacy gene expression data linear discriminant analysis gene selection methodology biomed central gene expression patterns microarray expression data mass spectrometry data multiple classier systems multivariate normal distribution full size image variable selection process gene expression studies mapping complex traits experiment-oriented pipeline variable importance returned gene selection accomplishes national taiwan university recalculate variable importances recalculating variable importances

Questions {❓}

Braga-Neto U, Hashimoto R, Dougherty ER, Nguyen DV, Carroll RJ: Is cross-validation better than resubstitution for ranking genes?
Ein-Dor L, Kela I, Getz G, Givol D, Domany E: Outcome signature genes in breast cancer: is there a unique set?

Schema {🗺️}

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         headline:Gene selection and classification of microarray data using random forest
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      headline:Gene selection and classification of microarray data using random forest
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         Algorithms
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