My research interests are in regulatory genomics and the application of machine learning and statistical modelling to understand and predict transcription factor binding sites and their link to diseases, with a particular focus on vector-borne diseases. For my PhD, I developed tools and techniques to elucidate, model, and evaluate the transcription factor binding specificity and occupancy for humans. I applied a combinatorial approach–combined multiple data and evidence–to model transcription factor binding and developed tools to evaluate binding models.
I am currently exploring transcriptional gene regulation of chemosensory genes in insect vectors, including tsetse and mosquitoes. This research entails analysing massive genomic data including, protein binding microarrays, ChIP-seq, and whole-genome sequences. In this research, we build machine learning models to scan whole genomes for potential binding sites, use multiple evidence lines to understand how olfaction is regulated. This information will allow us to design better vector management tools