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Discovery of diagnostic and prognostic molecular markers is important andactively pursued the research field in cancer research. For complex diseases,this process is often performed using Machine Learning. The current studycompares two approaches for the discovery of relevant variables: by applicationof a single feature selection algorithm, versus by an ensemble of diversealgorithms. These approaches are used to identify variables that are relevantdiscerning of four cancer types using RNA-seq profiles from the Cancer GenomeAtlas. The comparison is carried out in two directions: evaluating thepredictive performance of models and monitoring the stability of selectedvariables. The most informative features are identified using a four featureselection algorithms, namely U-test, ReliefF, and two variants of the MDFSalgorithm. Discerning normal and tumor tissues is performed using the RandomForest algorithm. The highest stability of the feature set was obtained whenU-test was used. Unfortunately, models built on feature sets obtained from theensemble of feature selection algorithms were no better than for modelsdeveloped on feature sets obtained from individual algorithms. On the otherhand, the feature selectors leading to the best classification results variedbetween data sets.

Analysis of ensemble feature selection for correlated high-dimensional RNA-Seq cancer data