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Motivation: Feature selection methods aim to reduce the complexity of data and to uncover the most relevant biological variables. In reality, information in biological datasets is often incomplete as a result of untrustworthy samples and missing values. The reliability of selection methods may therefore be questioned. Method: Information loss is incorporated into a perturbation scheme, testing which features are stable under it. This method is applied to data analysis by unsupervised featuredoi:10.1093/bioinformatics/btm528 pmid:17989091 fatcat:cjv6cwd7g5hixcs5tzohbjzon4