Promising New Tools and Techniques in Search for Alzheimer's Disease Biomarkers
Researchers at King's College London Undertake Text Mining Exercise with Instem; Research Published in Journal of Translational Medicine
CONSHOHOCKEN, PA - November 5, 2012 - - Instem, a leading provider of IT solutions to the global early development healthcare market, announced today that research, led by King’s College London’s Institute of Psychiatry and published in the Journal of Translational Medicine, is the first to use a new technology on a large scale to look for potential biomarkers for disease.
The new research proves that ’text mining’, or using the power of computers to ‘read’ the entire biomedical knowledge base, is a promising new tool in the search for Alzheimer’s disease biomarkers.
Alzheimer’s is the most common form of dementia, and currently affects approximately 500,000 people in the UK. During the course of Alzheimer’s disease, ‘tangles’ or ‘plaques’ develop in the brain leading to the death of brain cells. However, how and why these develop is still poorly understood and is likely to be due to a combination of factors, including age, genetics and environment. Identifying reliable biomarkers for the disease is important for developing early diagnostic tests, and finding new therapies.
Professor Simon Lovestone, lead author of the paper and Professor of Old Age Psychiatry at King’s College London says: “To our knowledge, this is the first time text mining has been used on this scale in the hunt for biomarkers. Essentially, we used the power of computers to ‘read’ everything that has ever been written in all biomedical science. We prove that text mining works, and we will take this forward in our hunt for Alzheimer’s biomarkers. Our results also demonstrate the value of large data in biomedical science; you could go beyond Alzheimer’s disease and use the same approach for other conditions where biomarkers are needed, from cancer to diabetes.”
Researchers at King’s worked with international colleagues and Instem (originally BioWisdom) to develop a series of ‘axioms’, or statements, about what a blood biomarker might look like. They then turned this into computer code and by using textual and linguistic analysis, searched for relevant information in all publicly available databases, combining neuro-imaging, genetic and proteomic data.
This derived a total of 25 potential biomarkers. The team then validated these - some had previously been identified as potential biomarkers, and in two other cases, they examined the proteins against large sample sets, and showed that the computer approach was correct.
Professor Lovestone adds: “So far, our search for Alzheimer’s disease biomarkers has focused either on an 'omics approach looking at as many proteins or genes as possible, or using a candidate approach looking for the obvious things. However, despite substantial international effort, neither has proved satisfactory. This technology offers an exciting and powerful new tool to advance our research in this field.”
Dr Jane Reed, Director of Life Sciences at Instem Scientific and co-author of the paper, says: “This research is a great example of academic-industry collaboration and shows the power of a translational approach (via data harmonization and integration) to re-use current and legacy data. There is a demand for better methods to predict biomarkers, and this paper validates our in- silico approach to biomarker discovery in human disease.”
Instem is a leading supplier of IT applications to the early development healthcare market, delivering compelling solutions for data collection, management and analysis across the R&D continuum. Instem applications are in use by customers worldwide, meeting the rapidly expanding needs of life science and healthcare organizations for data-driven decision making leading to safer, more effective products.
Instem’s portfolio of software solutions increases client productivity by automating study-related processes while offering the unique ability to generate new knowledge through the extraction and harmonization of actionable scientific information.
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