MRC DiMeN Doctoral Training Partnership: Application of machine learning approaches to analyse big and noisy datasets to understand the host response to lung and tissue damage by toxic agents

During the life course our lungs are continuously challenged by environmental contaminants and infectious agents resulting in airway remodeling and lifelong breathing complications. Understanding how these changes occur through mapping signaling pathways and changes in the host the response will prove critical in the design of effective therapeutic countermeasures, treatment and clinical care pathways.
The project will use and develop machine learning approaches to interrogate large transcriptomic and proteomic datasets that have been generated from experimental systems exposed to biological and chemical agents. This will be particularly focused on lung and blood tissues but may include data from other organs. The analysis and comparison of many thousands of data points can only be achieved through joining high resolution approaches with computational approaches. This project will focus on utilizing machine learning to provide a detailed map of the biological response to challenges and aid in the evaluation and selection of potential therapeutic countermeasures. The project will particularly develop the analysis of patterns from noisy data.
The project is supervised by Prof. Julian Hiscox at the University of Liverpool and Dr. Graeme Clark at the Defense Science Technology (dstl). The project will mainly be based at Liverpool with short research visits to dstl. Prof. Hiscox is Chair in Infection and Global Health and runs a dynamic laboratory of three post-doctoral researchers and nine PhD students. His group focuses on the host response to infectious/biological agents and the development of medical countermeasures. A particular focus is placed on high resolution data and computational/bioinformatics analysis. He has supervised twenty PhD students through to completion. Dr. Clark is within the CBR Division at dstl.