Machine learning using Gene-Sets to infer miRNA function

miRNA are regulators of cell phenotype, and there is clear evidence that these small posttranscriptional modifiers of gene expression are involved in defining a cellular response across states of development and disease. Classical methods for elucidating the repressive effect of a miRNA on its targets involve controlling for the many factors influencing miRNA action, and this can be achieved in cell lines, but misses tissue and organism level context which are key to a miRNA function. Also, current technology to carry out this validation is limited in both generalizability and throughput. Methodologies with greater scalability and rapidity are required to better understand the function of these important species of RNA. To this end, there is an increasing store of RNA expression level data incorporating both miRNA and mRNA, and in this chapter, we describe how to use machine learning and gene-sets to translate the knowledge of phenotype defined by mRNA to putative roles for miRNA. We outline our approach to this process and highlight how it was done for our miRNA annotation of the hallmarks of cancer using the Cancer Genome Atlas (TCGA) dataset. The concepts we present are applicable across datasets and phenotypes, and we highlight potential pitfalls and challenges that may be faced as they are used.