| Summary: | MicroRNAs (miRNAs) are a class of 22-nucleotide endogenous noncod- ing RNAs, plays important role in regulating target gene expression via repress- ing translation or promoting messenger RNAs (mRNA) degradation. Numerous re- searchers have found that miRNAs have serious effects on cancer. Therefore, study of mRNAs and miRNAs together through the integrated analysis of mRNA and miRNA expression profiling could help us in getting a deeper insight into the can- cer research. In this regards, High-Throughput Sequencing data of Kidney renal cell carcinoma is used here. The proposed method focuses on identifying mRNA- miRNA pair that has a signature in kidney tumor sample. For this analysis, Ran- dom Forests, Particle Swarm Optimization and Support Vector Machine classifier is used to have best sets of mRNAs-miRNA pairs. Additionally, the significance of selected mRNA-miRNA pairs is tested using gene ontology and pathway analysis tools. Moreover, the selected mRNA-miRNA pairs are searched based on changes in expression values of the used mRNA and miRNA dataset.
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