STARTED IN 1990 and completed in 2003, the goal of the Human Genome Project was to determine the sequence of chemical base pairs that make up human DNA and map all of the genes of the human genome. Through advocating a collaborative approach to data collection, scientists from all over the world engaged with the Project by contributing to its enormous databases. As a consequence, since the turn of the century, the volume of big data in health has grown at an extraordinary pace. The Project has set a precedent for the value of open-access data and proved an extremely useful tool in the development of personalised medicine.

MORAL MATTERSNow more than ever, scientists and medical professionals are aware that big data has the potential to improve survival rates of some of the world’s deadliest diseases. Unlike the open-access nature of the Human Genome Project, whose success depended on freely available resources, vast reams of health data remain underutilised and closely guarded in hospitals, where privacy and protection are prerogatives.

Dr Richard Frackowiak, Co-Director of The Human Brain Project – a new European collaborative initiative that aims to translate ICT advances into better tools for brain research – recently revealed to International Innovation the nature of these untapped collections of data hiding in our hospitals. “It’s a massive source of potential wealth – [and] all of these [privacy] issues are technically addressable and solvable,” he remarks. The full interview will be published in an upcoming issue of International Innovation focusing on neurology.

Many argue that the stringent protection surrounding patient databases must adapt to the needs of the medical community. In order for personalised medicine to evolve and normalise, as many records as possible must be collected and, in order for that information to be understood and exploited to its full potential, multidisciplinary partnerships must become the norm.

BRAIN BLUEPRINTSToday, with a significant proportion of health data locked away and unused, Frackowiak and his peers are searching for more innovative solutions to disease diagnosis and therapy through their dedication to the collection and application of big data.

Together, the Project researchers are attempting to redefine clinical practice in the face of devastating disease. Through creating intricate blueprints of the brain, Frackowiak and his team are aiming to develop their own medical version of Google. These maps are the first of their

kind and it is anticipated that they will make significant contributions to personalised medicine. By adopting a ‘bottom-up’ model, where each layer of proteins and chromosomes in the brain are examined for their different characteristics and corresponding risks to disease, an individual can expect remarkably specific diagnosis and treatment. This will save medical researchers considerable amounts of time and money, and will improve the momentum of bench-to-bedside projects.

Undoubtedly, in the future, ascertaining a more sophisticated diagnosis will rest upon our ability to more appropriately handle big data.

Big data is poised to make considerable advances in increasing the accuracy of disease detection, prevention and treatment. Here, International Innovation highlights the fundamental contributions made by the Human Genome Project and, more recently, the Human Brain Project – and explores the murky ethical waters of data collection

CRITICAL COLLABORATION

In order for data-driven healthcare to be possible, medicine, computing and analytic disciplines must work together to streamline disease therapy techniques. Here are just a number of ways big data analytics can support the goal of increasing cancer survival rates:

Accelerating annotationThe more genomes are annotated and the more cancers that are sequenced across a broader range of patients, the better our understanding of cancer survival will be.

Quality controlImproving the ways in which variant data are interpreted will also contribute to the drug discovery process.

Informed decisionsBy moving away from an organisational view of a disease or treatment programme, informed, multi-agency decision making will be more appropriate for patient-specific assessments.

After careThe role of population data will become increasingly important in improving patient survival predictions. This can be achieved by monitoring the performance of modelling algorithms that predict the length of time a patient is likely to survive and mapping it against population standards.

MEDICINE, MORALS AND BIG DATA

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ANALYSIS