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We invited Oxford East MP Anneliese Dodds to learn more about cutting edge research taking place in her constituency and learn more from researchers about the challenges they face.
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Bob Mahoney
Lunter Group: Functional Genomics & Machine Learning
Centre for Computational Biology
In the last decade biology has become a data-rich science. However, turning these data into an understanding of biology and disease remains challenging. Our group develops novel analytical methods to address a range of specific problems in genetics and sequence analysis, with the eventual goal to better understand the genetic basis of human biology in health and disease.
Sims Group: Computational Genomics
Centre for Computational Biology
We are a computational biology research group using genomic and functional genomic data to study transcriptional regulation in neuroscience.
Hughes Group: Genome Biology
Centre for Computational Biology MRC MHU
Using genomics, computational and synthetic biology approaches to understand how genes are regulated in health and disease.
Taylor Group: Analysis, Visualisation and Informatics
Centre for Computational Biology
Our group is responsible for the development and implementation of new ways to analyse, integrate, query and visualise large biomedical datasets. We work in collaboration with a wide variety of research groups both in and outside the MRC WIMM. We develop new methods and apply the latest bioinformatics techniques to public and in-house generated data including (but not limited to) genomics, genetics, transcriptomics, proteomics and imaging.
Sahakyan Group: Integrative Computational Biology and Machine Learning
Centre for Computational Biology
Combining computational biology, computational chemistry, and machine learning techniques with biological big data to unravel the higher genomic code of life.
Iotchkova Group: Statistical Genetics
Centre for Computational Biology
We focus on the development and application of new computational and statistical approaches aimed at utilizing high-dimensional datasets in biology and medicine to their full potential (eg by integration across different layers of information). In particular, we are keen to develop methodological advancements to accelerate the discovery and interpretation of multidimensional phenotypic consequences of common and rare genetic variation, as well as to use genetic information to infer direction of causality between different layers of phenotypic information.
Morrissey Group: Quantitative biology of cell fate and tissue dynamics
Centre for Computational Biology
The group develops mathematical and statistical modelling approaches to study the dynamics and fate choices of stem cells within tissues.
Koohy Group: Machine Learning and Integrative Approaches in Immunology
Centre for Computational Biology MRC HIU
We would like to understand the functional and molecular mechanisms of the immune system in various immunologically important conditions such as cancer, infection, autoimmune disease as well as ageing. We have a special interest in computational cancer immunotherapy such as antigen presentation, neo-antigen identification and T cell recognition of neo-antigens as well as interrogating the immune response to personalized vaccines from neo-antigens.