A new evolutionary microRNA marker selection using next-generation sequencing data

Next-generation sequencing allows high-throughput measurements of non-coding RNA expression levels in tissues. Analysis of microRNAs (miRNAs) is particularly effective in differentiation of cancerous tissue samples, based on patterns of their expression levels. The paper presents a wrapper feature selection approach based on t-Distributed Stochastic Neighbor Embedding (t-SNE), Covariance Matrix Adaptation Evolution Strategy (CMA-Es) and Support Vector Machine (SVM). The advantage of t-SNE is amplification of pairwise similarities by the means of t-Student neighborhood function. The attributes are embedded into 1-D space to reveal similarities between the features. Such information is used by CMA-ES through real-valued encoding in order to model pairwise relations between miRNAs with covariance matrices. Finally, the wrapper uses SVM to evaluate the objective, which expresses the tradeoff between classification quality and the desired number of features. The approach is tested on eight different cancer types from The Cancer Genome Atlas. It allows to find small sets of miRNAs to differentiate cancer types from a single tumor class to the normal one with high certainty.