From cancer gene expression data to simple vital rules

Microarray gene expression profiling technology generates huge high-dimensional data. Finding analysis techniques that can cope with such data characteristics is crucial in Bioinformatics. This paper proposes a variation of an ensemble learning approach combined with a clustering technique to extract "simple" and yet "vital" rules from genomic data. The paper describes the approach and evaluates it on cancer gene expression data sets. We report experimental results including comparisons with other results obtained from a similar ensemble learning approach as well as some sophisticated techniques such as support vector machines.