GEMLeR: gene expression machine learning repository. GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC curve, etc.) for classification, feature selection or clustering algorithms. This repository was inspired by an increasing need in machine learning / bioinformatics communities for a collection of microarray classification problems that could be used by different researches. This way many different classification or feature selection techniques can finally be compared to eachother on the same set of problems.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Smith, Michael R.; Martinez, Tony; Giraud-Carrier, Christophe: An instance level analysis of data complexity (2014) ioport
- Tew, C.; Giraud-Carrier, C.; Tanner, K.; Burton, S.: Behavior-based clustering and analysis of interestingness measures for association rule mining (2014)