

![0_image_0.png](0_image_0.png)

therefore the Dram2008 set comprises 28 genes. The set of relevant attributes obtained with Boruta based on N trees is further referred to as BorN. Boruta500 set contains 82 genes, whereas the Boruta100k contains 261 genes.

The comparison of genes deemed important by four methods is presented in Figure 6. One can easily notice, that all results of three older algorithms differ significantly. Five genes were unique to Gol1999, six to Dram2008 and 41 to Dud2002. Ten attributes are found in all sets, four are common present in Gol1999 and Dram2008 but not in Dud2002, thirty one are present in Gol1999 and Dud2002 but not in Dram2008.

The Bor500 set contains all genes from Dram2002, including six genes unique to this set. It contains also two genes unique to Gol1999, twenty genes unique to Dud2002, as well as sixteen genes contained both in Gol1999 and Dud2002. There are sixteen genes unique to Bor500. Interestingly, two algorithms using tree-based importance agree very well despite significant differences in the implementation and importance definition.

The Bor100k set, as well as Bor50k set, contain all attributes found in all other sets, with the exception of a single gene present uniquely in Dud2002; additionally more than 150 new genes were found in Bor100k. Despite all differences between results of previous studies, results of Boruta procedure confirms that the genes found with the older methods are indeed relevant. On the other hand this agreement confirm the robustness of the Boruta algorithm. The biological analysis of the new genes found by Boruta is beyond the scope of the current work.

The results of this experiment show that the sensitivity of the Boruta algorithm depends on the number of trees in the random forest ensemble. This happens due to the properties of the importance measure - it is estimated as