A new evolutionary rough fuzzy integrated machine learning technique for microRNA selection using next-generation sequencing data of breast cancer

MicroRNAs (miRNA) play an important role in various biological process by regulating gene expression. Their abnormal expression may lead to cancer. Therefore, analysis of such data may discover potential biological insight for cancer diagnosis. In this regard, recently many feature selection methods have been developed to identify such miRNAs. These methods have their own merits and demerits as the task is very challenging in nature. Thus, in this article, we propose a novel wrapper based feature selection technique with the integration of Rough and Fuzzy sets, Random Forest and Particle Swarm Optimization, to identify putative miRNAs that can solve the underlying biological problem effectively, i.e. to separate tumour and control samples. Here, Rough and Fuzzy sets help to address the vagueness and overlapping characteristics of the dataset while performing clustering. On the other hand, Random Forest is applied to perform the classification task on the clustering results to yield better solutions. The integrated clustering and classification tasks are considered as an underlying optimization problem for Particle Swarm Optimization method where particles encode features, in this case, miRNAs. The performance of the proposed wrapper based method has been demonstrated quantitatively and visually on next-generation sequencing data of breast cancer from The Cancer Genome Atlas (TCGA). Finally, the selected miRNAs are validated through biological significance tests. The code and dataset used in this paper are available online1.