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Programming smart molecules
Date:12/12/2013

on efficiently translating particular algorithms that have been successful at solving difficult problems in areas like robotics into molecular descriptions," Napp explains. "For example, these algorithms allow today's robots to make complex decisions and reliably use noisy sensors. It is really exciting to think about what these tools might be able to do for building better molecular machines."

Indeed, the field of machine learning is revolutionizing many areas of science and engineering. The ability to extract useful insights from vast amounts of weak and incomplete information is not only fueling the current interest in "big data," but has also enabled rapid progress in more traditional disciplines such as computer vision, estimation, and robotics, where data are available but difficult to interpret. Bioengineers often face similar challenges, as many molecular pathways are still poorly characterized and available data are corrupted by random noise.

Using machine learning, these challenges can now be overcome by modeling the dependencies between random variables and using them to extract and accumulate the small amounts of information each random event provides.

"Probabilistic graphical models are particularly efficient tools for computing estimates of unobserved phenomena," says Adams. "It's very exciting to find that these tools map so well to the world of cell biology."


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Contact: Caroline Perry
cperry@seas.harvard.edu
617-496-1351
Harvard University
Source:Eurekalert  

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