Analysis of ensemble feature selection for correlated high-dimensional RNA-Seq cancer data
This work addresses biomarker discovery for cancer diagnosis using machine learning, but it is incremental as it shows no advantage for ensemble methods in this context.
The study compared single versus ensemble feature selection methods for identifying cancer biomarkers from RNA-seq data, finding that ensemble methods did not improve predictive performance over individual algorithms, though stability varied with U-test performing best.
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.