Microarray data analysis has been shown to provide an effective tool for 
studying cancer and genetic diseases. Although classical machine learning 
techniques have successfully been applied to find informative genes and to 
predict class labels for new samples, common restrictions of microarray analysis 
such as small sample sizes, a large attribute space and high noise levels still 
limit its scientific and clinical applications. Increasing the interpretability 
of prediction models while retaining a high accuracy would help to exploit the 
information content in microarray data more effectively. For this purpose, we 
evaluate our rule-based evolutionary machine learning systems, BioHEL and 
GAssist, on three public microarray cancer datasets, obtaining simple rule-based 
models for sample classification. A comparison with other benchmark microarray 
sample classifiers based on three diverse feature selection algorithms suggests 
that these evolutionary learning techniques can compete with state-of-the-art 
methods like support vector machines. The obtained models reach accuracies above 
90% in two-level external cross-validation, with the added value of facilitating 
interpretation by using only combinations of simple if-then-else rules. As a 
further benefit, a literature mining analysis reveals that prioritizations of 
informative genes extracted from BioHEL's classification rule sets can 
outperform gene rankings obtained from a conventional ensemble feature selection 
in terms of the pointwise mutual information between relevant disease terms and 
the standardized names of top-ranked genes.