  Gene selection is an important part of microarray data analysis because it
provides information that can lead to a better mechanistic understanding of an
investigated phenomenon. At the same time, gene selection is very difficult
because of the noisy nature of microarray data. As a consequence, gene
selection is often performed with machine learning methods. The Random Forest
method is particularly well suited for this purpose. In this work, four
state-of-the-art Random Forest-based feature selection methods were compared in
a gene selection context. The analysis focused on the stability of selection
because, although it is necessary for determining the significance of results,
it is often ignored in similar studies.
  The comparison of post-selection accuracy in the validation of Random Forest
classifiers revealed that all investigated methods were equivalent in this
context. However, the methods substantially differed with respect to the number
of selected genes and the stability of selection. Of the analysed methods, the
Boruta algorithm predicted the most genes as potentially important.
  The post-selection classifier error rate, which is a frequently used measure,
was found to be a potentially deceptive measure of gene selection quality. When
the number of consistently selected genes was considered, the Boruta algorithm
was clearly the best. Although it was also the most computationally intensive
method, the Boruta algorithm's computational demands could be reduced to levels
comparable to those of other algorithms by replacing the Random Forest
importance with a comparable measure from Random Ferns (a similar but
simplified classifier). Despite their design assumptions, the minimal optimal
selection methods, were found to select a high fraction of false positives.
