Background: Feature selection is an important pre-processing task in the analysis of complex data. Selecting an appropriate subset of features can improve classification or clustering and lead to better understanding of the data. An important example is that of finding an informative group of genes out of thousands that appear in gene-expression analysis. Numerous supervised methods have been suggested but only a few unsupervised ones exist. Unsupervised Feature Filtering (UFF) is such a method, based on an entropy measure of Singular Value Decomposition (SVD), ranking features and selecting a group of preferred ones.Results: We analyze the statistical properties of UFF and present an efficient approximation for the calculation of its entropy measure. This allows us to develop a web-tool that implements the UFF algorithm. We propose novel criteria to indicate whether a considered dataset is amenable to feature selection by UFF. Relying on formalism similar to UFF we propose also an Unsupervised Detection of Outliers (UDO) method, providing a novel definition of outliers and producing a measure to rank the "outlier-degree" of an instance.Our methods are demonstrated on gene and microRNA expression datasets, covering viral infection disease and cancer. We apply UFFizi to select genes from these datasets and discuss their biological and medical relevance.Conclusions: Statistical properties extracted from the UFF algorithm can distinguish selected features from others. UFFizi is a framework that is based on the UFF algorithm and it is applicable for a wide range of diseases. The framework is also implemented as a web-tool.The web-tool is available at: http://adios.tau.ac.il/UFFizi.
Bibliographical noteFunding Information:
We thank Alon Kaufman and Nati Linial for stimulating discussions and suggestions. RV is a fellow member of the Sudarsky Center for Computational Biology. Funding: A.G. is an Edmond J. Safra fellow. This work was supported by the EU Framework VII Prospects consortia