Unsupervised feature selection under perturbations: Meeting the challenges of biological data

Roy Varshavsky*, Assaf Gottlieb, David Horn, Michal Linial

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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 feature filtering (UFF). The latter has been shown to be a very successful method in analysis of gene-expression data. Results: We find that the UFF quality degrades smoothly with information loss. It remains successful even under substantial damage. Our method allows for selection of a best imputation method on a dataset treated by UFF. More importantly, scoring features according to their stability under information loss is shown to be correlated with biological importance in cancer studies. This scoring may lead to novel biological insights.

Original languageAmerican English
Pages (from-to)3343-3349
Number of pages7
Issue number24
StatePublished - Dec 2007

Bibliographical note

Funding Information:
This research is supported by EU FR6 DIAMONDS consortium. R.V. is awarded a fellowship by the SCCB, the Sudarsky Center for Computational Biology of the Hebrew University of Jerusalem.


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