Image and Content Source: Med-Tech Innovation News
One firm believes the solution could lie in bespoke, semi-automated software that combines artificial intelligence approaches, including semantic searching and machine learning, to sift through tens of millions of documents to identify genes, diseases, devices and many more scientific concepts.
The semantic platform developed by Cambridge-based technology company SciBite, which is being piloted in partnership with medical research charity LifeArc, enables researchers to gain early insights and uncover information such as novel technologies, new drug targets/biomarkers and rare disease connections. The two firms say that this will improve the ability for researchers to identify emerging mega trends in medical research, with the ultimate aim of accelerating discoveries in the healthcare space.
Neal Dunkinson, head of technical sales from SciBite said: “It’s almost impossible to keep ahead of the volumes of data being created second to second by the life science industry. Our technology is designed to reduce the time spent mining data by up to 80%, providing researchers with a subset of scientifically-relevant information filtered from the vast amounts of raw data in a rapid, easy-to-interpret manner, allowing them to focus and accelerate their research. As you can imagine, this is a game-changer in the healthcare space where identifying valid new leads is highly-competitive”.
SciBite and LifeArc have recently completed the first pilot study of its kind and have collaborated to build custom-made linguistic patterns to optimise the horizon scanning process with two key aims in mind:
Ben Cryar, senior analyst in the opportunity assessment group at LifeArc said: “This AI technology is set to revolutionise medical research capabilities. Over the next few years we hope to have identified many new areas of research and invest £30M with our new Seed and Philanthropic funds to support innovative research in the UK”.
Search just got a whole lot more powerful.Read
What exactly is "ontology alignment"? Why is it important and how does it differ from other text-analytics methods? In this post we explore this important but often overlooked topic and discuss its relevance to the work of SciBite and other groups such as FAIR and the Pistoia Alliance within the broader scientific community.Read
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