Accepting PhD Students

PhD projects

1. Next-generation computational method for Genomics Risk Assessment and Stratification in multi-way admixed and structured populations. 2. Developing improved statistical and computational models for the association tests of genetic variation versus drug response, genetic variation versus individualized dosage regimens of potentially toxic drugs accounting different environment factors. 3. Leveraging evolutionary convergence, polygeneticity and population structure in Bacterial Genome-wide association studies. 4. Computational model for pinpointing accurately individual's ancestry along its genome or on a specific chromosomal region to cope with populations that have a complex admixture history and adaptation event. 5. Computational models for leveraging cross-population gene/sub-network meta-analysis to recover disease association signal risk, cross-population gene/sub-network drug responses and regulations in homogeneous or recently admixed populations.

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Personal profile

Research interests

My research has a focus on methodologies of improving the analysis of large-scale genomic studies such as genome-wide association, fine-mapping studies, admixture mapping, genetics of mixed ancestry populations and analysing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. My interest is to unlock genomics in healthcare through computational and statistical methods to understand the genetics and environment architecture of genetic diseases, variation in drug/treatment response. This interest is extending in methodologies and developing tools for deconvoluting human genetic diversity to uncover the role of genetic and environmental factors in determining risk and susceptibility of communicable and non-communicable diseases. I develop Bioinformatics methods/tools pertinent to genetic and environmental determinants of susceptibility to complex diseases, drug responses, genome variation, microbial omics, and infectious diseases from both the host and pathogen perspectives to facilitate the transformation of genomics-driven clinical practice.

Our current range of developed Bioinformatics tools from functional genomics, post-Genome-wide Association Studies, disease risk prediction, Gene Ontology semantic similarity can be found at (https://github.com/echimusa).

I am actively involved in the development of bioinformatics and Omics Data Science educational courses to offer a unique opportunity in the use of bioinformatics methods to access and harness large complicated high-throughput data and uncover meaningful information that could be used to understand molecular mechanisms and develop novel targeted therapeutics/diagnostic tools.

Biography

Prof. Emile R. Chimusa is an internationally recognised computational population genomics and bioinformatics researcher and an experienced educator. He has recently joined Northumbria from the University of Cape Town, where he served as Associate Professor and Programme director of PGDip/MSc/PhD in Computational Health Informatics and Honours in Human Genetics and Forensic Genetics.

He has been a part of a wide array of International Research Consortiums that has received both research and industry focused funding from the South African National Research Foundation, South African Council for Scientific and Industrial Research, Newton Fund, Canadian International Development Fund, NIH, The Wellcome Trust and Health~Holland. In total, he has received over £17.7 million in research funding as PI or Co-investigator. His research has been published in high-impact international journals and presented both at major scientific conferences and in broadcast, print, and web-based media worldwide. He has a record of accomplishment in designing and developing Computational and Bioinformatics tools (https://github.com/echimusa).

Education/Academic qualification

Bioinformatics, PhD

1 Jul 201331 Dec 2099

Award Date: 1 Jul 2013

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