A research on machine learning based gene selection algorithms for RNA-seq data.

Discovering differentially expressed genes (DEGs) is an essential process for diagnosis and treatment. In this study, we evaluate the capacity of machine learning algorithms to identify genes that are simulated as truly differentially expressed. We first obtain nine real datasets from The Cancer Genome Atlas Program. We conduct Monte Carlo simulation by exploring the characteristics of the real datasets (i.e. the number of genes). Since these datasets have highly imbalanced class distributions, datasets with the same characteristics are also generated with balanced class ratios. In this way, 18 scenarios are obtained with a total number of 19,947 genes of which 10 genes are simulated significantly different between the two groups. After the pre-processing steps including filtering, normalization, transformation and univariate analysis, gene selection is conducted with five machine learning algorithms. These are biosigner algorithm, GMDH-type neural network algorithm, Determan's optimal gene selection algorithm with random forest, support vector machines and elastic net generalized linear models. Five-fold cross validation is used for improving gene selection performances. Recall, precision and F-measure are taken into account for the evaluation of gene selection performances. All algorithms show higher average performance in balanced scenarios than in unbalanced scenarios. Elastic net generally achieves favorable average performance in both balanced and unbalanced scenarios.