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A Hackathon Demonstrates the Need for Deep Learning in R&D
Posted on January 12th, 2018 by Tim Hoctor in Pharma R&D
Today’s life science companies find themselves dealing with an interesting dilemma. With the help of many technological breakthroughs, they now have access to tons of data bursting with potential. The downside is that most organizations still don’t have the means to read, process, and interpret those piles of data in a way that is useful and efficient. There are techniques like deep learning, text mining and AI that could help ‘connect the dots’ between data so that it can be used to discover medicines and help patients, but these methods are still being developed and refined. Furthermore, they don’t necessarily solve another significant problem: a lack of common data standards.
Last year, when Elsevier sponsored a challenge at a “hackathon” in London, it ended up providing an apt case study for the situation. The challenge was for students and researchers to help the UK-based charity Findacure by advancing research for Friedreich’s Ataxia, a rare neurodegenerative disease. Attempting to accelerate drug repurposing for an orphan disease is quite a serious challenge to begin with, but the participants found themselves up against another frustrating problem that slowed them down before they had even started: the data. The trouble was that they had a huge amount of data to contend with, and it was in disparate formats. In some cases, simply getting the files open was an obstacle.
Take a look at the article Solving the Data Riddle to Yield Hope for Orphan Disease recently featured in Applied Clinical Trials to learn more about the hackathon team’s struggles with data, what they did to overcome the challenges and what they learned in the process about the future of R&D.
All opinions shared in this post are the author’s own.
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