  The discovery of disease biomarkers from gene expression data has been
greatly advanced by feature selection (FS) methods, especially using ensemble
FS (EFS) strategies with perturbation at the data level (i.e., homogeneous,
Hom-EFS) or method level (i.e., heterogeneous, Het-EFS). Here we proposed a
Hybrid EFS (Hyb-EFS) design that explores both types of perturbation to improve
the stability and the predictive power of candidate biomarkers. With this,
Hyb-EFS aims to disrupt associations of good performance with a single dataset,
single algorithm, or a specific combination of both, which is particularly
interesting for better reproducibility of genomic biomarkers. We investigated
the adequacy of our approach for microarray data related to four types of
cancer, carrying out an extensive comparison with other ensemble and single FS
approaches. Five FS methods were used in our experiments: Wx, Symmetrical
Uncertainty (SU), Gain Ratio (GR), Characteristic Direction (GeoDE), and
ReliefF. We observed that the Hyb-EFS and Het-EFS approaches attenuated the
large performance variation observed for most single FS and Hom-EFS across
distinct datasets. Also, the Hyb-EFS improved upon the stability of the Het-EFS
within our domain. Comparing the Hyb-EFS and Het-EFS composed of the
top-performing selectors (Wx, GR, and SU), our hybrid approach surpassed the
equivalent heterogeneous design and the best Hom-EFS (Hom-Wx). Interestingly,
the rankings produced by our Hyb-EFS reached greater biological plausibility,
with a notably high enrichment for cancer-related genes and pathways. Thus, our
experiments suggest the potential of the proposed Hybrid EFS design in
discovering candidate biomarkers from microarray data. Finally, we provide an
open-source framework to support similar analyses in other domains, both as a
user-friendly application and a plain Python package.
