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How IBM Watson Can Help Us Find a Cure for ALS

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How IBM Watson Can Help Us Find a Cure for ALS

We hope that the use of IBM Watson for Drug Discovery will allow us to identify new and more effective treatments for ALS.

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The last year has seen a growing number of fascinating breakthroughs in the use of AI to spot and diagnose disease.  One of the latest of these has arrived via a project conducted by the Barrow Neurological Institute and IBM that has spotted new genes linked to Lou Gehrig’s disease (ALS).  The team hope that the discovery will help pave the way for new drug research.

“We are very excited about this discovery,” the team say. “ALS is one of the most complicated diseases to unravel and there is no cure. We hope that the use of IBM Watson for Drug Discovery will allow us to identify new and more effective treatments for ALS.”

Inside ALS

ALS occurs when the cells that control voluntary muscle movements begin to die.  The degenerative disease leads to paralysis, and ultimately death, but scientists are still largely unsure about the causes of it, with current treatments largely failing to offer much in the way of assistance.  This leads to a life expectancy of those diagnosed with the disease of around 3-5 years.

The team used Watson for Drug Discovery to try and identify genes and proteins that may play a part in ALS.  The algorithm was trained on known proteins linked to the disease together with the published literature on ALS.

The algorithm then produced a ‘league table’ of the 1,500 or so genes in the human genome in order of their likely connection or influence on ALS.  The top candidates were then analyzed in person by the Barrow team, who found that 8 of the top 10 genes had a connection to ALS.

More interestingly however, the process uncovered 5 genes that had previously not been associated with ALS.  What’s more, it managed this in a few months, as opposed to the years it would have taken researchers going about things in a more traditional manner.

“We could have individually looked at the 1,500 proteins and genes but it would have taken us much longer to do so,” the team say. “IBM Watson for Drug Discovery, with its robust knowledge base, was able to rapidly give us new and novel information we would not otherwise have had.”

The project is part of a growing trend to make drug discovery more efficient.  New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping $2.6 billion.

It’s a topic I’ve covered before, with a study published last year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%.

A nice example of what could be possible is provided by a recent study published in Cell Chemical Biology. The study reveals a big data based approach to detecting toxic side effects that would prohibit a drug from being used on humans before it gets to the expensive clinical trial stage.

So the IBM project is very much in the zeitgeist that promises to revolutionize how healthcare is managed, especially if we can ensure that enough data is made available.

“Traditional research tools are fast becoming inadequate to help data scientists and researchers keep pace with and find relevant insights among the now billions of documents which are spread all over the world,” IBM say. “Watson for Drug Discovery can help organizations far more rapidly pinpoint the most promising paths to drug discovery. We are honored to support Barrow’s efforts to identify the underlying cause of ALS.”

Check out the video below to see more about the project.


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Topics:
drugs ,watson ,healthcare ,big data

Published at DZone with permission of Adi Gaskell, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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