Multivariate gene selection: Does it help

When building predictors of disease state based on gene expression data, gene selection is performed in order to achieve a good performance and to identify a relevant subset of genes. Although several gene selection algorithms have been proposed, a fair comparison of the available results is very problematic. This mainly stems from two factors. First, the results are often biased, since the test set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Therefore, no generally applicable conclusions can be drawn. We therefore adopted an unbiased protocol to perform a fair comparison of state of the art multivariate and univariate gene selection techniques, in combination with a range of classifiers. Our conclusions are based on seven gene expression datasets, across many cancer types. Surprisingly, we could not detect any signi...

• Type: Conference
• Year: 2005
• Where: CSB
• Authors: Carmen Lai, Marcel J. T. Reinders
• Keywords: Bioinformatics, CSB 2005, Gene Expression, Gene Selection, Gene Selection Algorithms