The Medical Bioinformatics Research Section (MBRS, ‘embers’) is a thematic specialism within The Patrick G Johnston Centre for Cancer Research with the goal of advancing research at the interface of computer science, statistics, cell biology and cancer medicine.
There is a huge quantity of information available to biomedical scientists, including DNA sequences from thousands of people. Making sense of the billions of DNA bases is not trivial; moreover, DNA sequencing is just a fraction of the available data. For example, huge imaging, transcriptome, proteome and epigenome profiling datasets are currently stored in publicly funded databases. This energises our research, which applies computational approaches necessary to draw together and make sense of the information held in these datasets - in order to generate scientific advances for the benefit of human health.
We are very happy to hear from people interested in applying for, or who already have, competitive Fellowships to join MBRS. Please contact Ian Overton (Section Head) in the first instance to discuss options.
Dr Jaine Blayney
We work across inflammatory disease and cancer, characterising molecular subtypes corresponding to patient prognosis, aiming to develop tests that can stratify patients based on predicted response to therapeutics. We focus on the reanalysis of existing data and the repurposing of statistical and computing methods from other domains.Find out more
Dr Darragh McArt
We work in a range of bioinformatic areas that integrate information to common platform spanning concepts from evolutionary algorithms and natural language processing to phylogenetics and evolution. This has recently culminated in the QUB spin out company Sonrai Analytics, an integrative software solution for modern day ‘big data’.Find out more
Data Intensive Biomedicine
Dr Ian Overton
We study molecular control of phenotype, with emphasis on metastasis and drug resistance. Analysing high dimensional datasets, we map the complex networks that control cell behaviour and develop algorithms for patient stratification. These activities both inform fundamental biology and work towards advances in cancer medicine.Find out more