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Translational Bioinformatics is the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data, into proactive, predictive, preventive, and participatory health. Here at the DSI our translational bioinformatics team work on an array of projects. 

Translational Bioinformatics looks to take the ever increasing volume of biomedical data and use it to improve human health. It is of particular relevance to precision medicine, the idea of adapting care and treatment to individual patients. 
The last decade has seen an explosion in the information available about the health and physiology of people. From ever-cheaper and ever-faster genetic sequencing, through sophisticated molecular analysis of metabolites to personal fitness and medical devices, we are faced with a cornucopia of information that could potentially be used. But understanding this information is not straightforward. There are practical issues of handling and comparing such massive and varied data, and scientific issues in how to best interpret and understand the patterns seen.

The Translational Bioinformatics group engages with these problems on a number of levels, from the lab bench to the hospital bedside. We have developed a number of software platforms to store and analyse complex biomedical data, using computationally intense methods. In collaboration with hospitals and clinicians, we gathering information on different diseases and conditions, making large cohorts for understanding complex conditions. Finally, we are developing new algorithms for analysis, including graphical models and algorithms for integrating multiple levels of information. 

Multidisciplinary Applications

Personalized Medicine

Personalised medicine is about tailoring medical treatment to the individual characteristics of each patient, by classifying individuals into groups that differ in their susceptibility to a particular disease or their response to a specific treatment.  With the amount of data being mined and analysed, it will be easier to identify genetic correlations, identify patterns in patient and population data, identify patient specific patterns and predict physiological conditions, discovering biomarkers that present signs of normal or abnormal processes and provide better patient self-management for enhanced clinical outcomes. The proliferation of data, generated from high-throughput molecular profiling to physiological sensing, offers great opportunities for personalised medicine by offering more precise diagnoses and more effective treatments, as researchers are able to drill down to see what is happening and create more targeted therapies, specifically at the molecular and tissue levels.

At the DSI we are working with Imperial College’s biomedical researchers in the medical school and the worldwide medical research community to build big data technologies to enable personal medicine.  This research includes building the European Translational Knowledge Management and Service (eTRIKS) platform as the gold standard personalised medicine big data research platform for the European medical research community.

Check out some of our case studies on personalized medicine

Understanding Biology

With the advances in high-throughput experimental technologies, biologists are starting to grapple with massive data sets. Multi-omics data such as genomics, lipidomics, proteomics, transcriptomics and metabolomics data are growing astronomically. These data create a potential bonanza, as they provide systems-level measurements for virtually all types of cellular components in a model organism, yielding unprecedented views of the cellular inner workings. Meanwhile, these data also raise many new research challenges, not only because of their size, but also their increasing complexity. 

At the DSI, we are working with biologists and quantitative scientists including statisticians, mathematicians, engineers and computer scientists, to enable next generation bioinformatics. To make sense of ‘omics’-scale data and get insights from these data that will advance our understanding of the underlying biological mechanisms, different areas including systems biology, statistics, machine learning and high performance computing, are brought together in the DSI to explore the fundamental questions in biology. Further it is expected to lead to practical applications in medicine, drug discovery, and bioengineering.

Natural Environment

Where understanding the human body and biological processes is a complex and data-intensive challenge, consider scaling up our understanding of the complexities of nature to the environment around us, from the climate to our planet. Simulation, modelling and knowledge discovery needs to operate at every biological scale extending up to a global scale that not only includes biological scales (from microscopic upwards), but also behavioural complexities of interacting species, geophysical and meteorological systems, and biodiversity and even the spread of disease. 

At the DSI, we are working with environmental researchers to understand how to deal with their data, apply new statistical techniques, and lower the barriers to harnessing complex data in their research by building new tools and training scientists in data science methods. We are collaborating with world-leading research groups and centres, such as Imperial’s own Space and Atmospheric Physics Group, and the Grantham Institute for Climate Change, to help them harness the power of big data in climate prediction, risk mitigation, and natural disaster planning.

Projects concerning Translational Bioinformatics




U-BIOPRED: Unbiased BIOmarkers in PREDiction of respiratory disease outcomes


Using samples and medical information from hundreds of adults and children, the project aims to identify different sub-types of severe asthma.

Check out the U-BIOPRED website here.



European Translational Information and Knowledge Management Services (eTRIKS)

We are developing a sustainable open-source data and knowledge management platform to support translational research. TranSMART, an open-source knowledge management platform combining a data repository with a suite of search and analysis tools, is a core component of the eTRIKS platform development.

See the eTRIKS website here.

Check out the tranSMART foundation here



Image result for biogen idecOPTIMISE is a Joint Working collaboration between Imperial College London and the biopharmaceutical company Biogen Idec.  The OPTIMISE project develops and deploys tools for collecting a wide range of data from people with MS in addition to routine clinical assessments.   The project works to integrate brain scans, genomics data, biomarkers from blood samples, self-reported quality of life measures and data from sensors that track movement into a single database

Read more about the project here