Developing pipelines for the inference of gene regulatory networks
Gene regulatory networks are fundamental to many processes that take place in the cell. However learning the structure of these networks from high-throughput experimental data and deriving useful biological insights is challenging. There are typically far fewer data points than there are genes in the genome, and so it is necessary to reduce the dimensionality of the data as well as applying methods that are robust when there are few samples. The project will involve adapting and expanding on existing methods to build a pipeline for the analysis of gene expression microarray or RNA-seq data. Depending on the research interests of the student this could involve investigation of parallel computing approaches on GPUs or CPU clusters, or developing novel statistical or machine learning methods.