I was talking to a cancer researcher recently and he points out that many of their challenges are indeed algorithmic. But he also brings up the contrapositive. Since we don't have great algorithms now, we don't know how to make sense of DNA sequences and in particular don't know how to map genetic markers to an appropriate cancer treatment. He works with cancer patients, knowing he can't give them the best possible treatment, not because of lack of data, but due to lack of ways to analyze that data. People die because we don't have the ability to break through the complexity of these algorithmic challenges.
Monday, August 25, 2014
A Deadly Side of Complexity
Better algorithms can lead to better medicine and save lives. Just today Tim Gowers discusses Emmanuel Candès' ICM Plenary Lecture, which among other things describes how Candès' work on compressed sensing leads to shorter MRI scans for children, greatly reducing the risk of oxygen deprivation. Prove P = NP with a practical algorithm, and you'll conquer that worst of our diseases. Sounds great until you realize what we can't do.
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This is an excellent view. A question often asked is how is the ordinary life touched by theoretical computer science. Would you not like to extend this post to answer such questions? Being able to make the connections between the algorithmic challenges and the challenges of life is evidence enough that this field is not all ivory towers, however theoretical the research happens to appear.
ReplyDeleteAn interesting direction here would be to open that data that lacks analysis, so that the large community of people that care about algorithms could contribute - some link for steps in that direction that look interesting are http://www.cbioportal.org/public-portal/data_sets.jsp, http://opencancer.cc/ and https://ocg.cancer.gov/programs/target, in perceived decreasing order of openness...
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