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	GeneSrF

GeneSrF: gene selection with random forests

GeneSrF is a web tool for gene selection in classification problems that uses random forest. Two approaches for gene selection are used: one is targeted towards identifying small, non-redundant sets of genes that have good predictive performance. The second is a more heuristic graphical approach that can be used to identify large sets of genes (including redundant genes) related to the outcome of interest. The first approach is described in detail in this paper. The R code is available as an R package from CRAN or from this link. For further details see the help.

To use this web tool you need to provide two files, one with the gene expression data and one with the class labels, and press "Submit".

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Input files

Expression data file: (?)
Class file: (?)

Push the "Submit" to start execution.

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Help

Citing GeneSrF

If you use GeneSrF, please cite it in your publications. Please provide the URL and the publication:

Diaz-Uriarte, R. 2007. GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest. BMC Bioinformatics 2007, 8:328.

Source code availability

See GitHub repository for GeneSrF and GitHub repository for varSelRF.

UPDATE and disk image availability: Minor updates fixing/updating some links, improving some of the logic, using R-4.5.1, and allowing to use https, on September and October 2025. I have a QCOW2 disk image containing pomelo2, signs2, genesrf, and tnasas, along with a libvirt XML configuration file. This would let you run the environment locally using KVM/QEMU and modify the setup as needed. Let me know if you'd like a copy. (Note, though, that most of the code is from over 15 years ago, and it is still using Python 2.7. The code would benefit from a major clean up, so this was just a minimal, hackish intervention to allow reviving the apps on a virtual machine).