RNA-Sequencing (RNA-Seq) delivers a complete snapshot of gene expression in a cell, with a single experiment containing expression levels of tens of thousands of genes from multiple hundred samples. The nature of gene expression data, however, poses challenges to its analysis in terms of its high dimensionality, noise, and complexity of the underlying biological processes to detect. Researchers aim to identify genes that reliably discriminate sample groups from each other, e.g. a cancerous from a healthy one. The current state-of-the-art for gene selection is to apply traditional statistical and machine learning approaches, e.g. Support Vector Machines (SVM).
