Gene selection is an important part of microarray data analysis because it provides information thatcan 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 isparticularly well suited for this purpose.
In this work, four state-of-the-art Random Forest-basedfeature selection methods were compared in a gene selection context. The analysis focused on thestability of selection because, although it is necessary for determining the significance of results, it isoften ignored in similar studies.
Results:
The comparison of post-selection accuracy in the validation of Random Forest classifiers revealed thatall investigated methods were equivalent in this context.
However, the methods substantially differedwith 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.
Conclusions:
The post-selection classifier error rate, which is a frequently used measure, was found to be apotentially deceptive measure of gene selection quality.
When the number of consistently selectedgenes was considered, the Boruta algorithm was clearly the best. Although it was also the mostcomputationally intensive method, the Boruta algorithm’s computational demands could be reducedto levels comparable to those of other algorithms by replacing the Random Forest importance witha comparable measure from Random Ferns (a similar but simplified classifier).
Despite their designassumptions, the minimal-optimal selection methods, were found to select a high fraction of falsepositives.
Author: Miron Bartosz Kursa
Credits/Source: BMC Bioinformatics 2014, 15:8
Published on: 2014-01-13
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