  Discovery of diagnostic and prognostic molecular markers is important and
actively pursued the research field in cancer research. For complex diseases,
this process is often performed using Machine Learning. The current study
compares two approaches for the discovery of relevant variables: by application
of a single feature selection algorithm, versus by an ensemble of diverse
algorithms. These approaches are used to identify variables that are relevant
discerning of four cancer types using RNA-seq profiles from the Cancer Genome
Atlas. The comparison is carried out in two directions: evaluating the
predictive performance of models and monitoring the stability of selected
variables. The most informative features are identified using a four feature
selection algorithms, namely U-test, ReliefF, and two variants of the MDFS
algorithm. Discerning normal and tumor tissues is performed using the Random
Forest algorithm. The highest stability of the feature set was obtained when
U-test was used. Unfortunately, models built on feature sets obtained from the
ensemble of feature selection algorithms were no better than for models
developed on feature sets obtained from individual algorithms. On the other
hand, the feature selectors leading to the best classification results varied
between data sets.
