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Teaching Computers to Guide Science: New Machine Learning Method Sees the Forests and the Trees

A fork in a road through a forest.

A novel machine learning method called iterative Random Forests enables scientists to derive insights from systems of previously intractable complexity in record time.

Credit: iStock

Researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and University of California, Berkeley have created a novel machine learning method that enables scientists to derive insights from highly complex systems in record time.

In a paper published recently in the Proceedings of the National Academy of Sciences, the researchers describe a technique called "iterative Random Forests," which they say could have a transformative effect on any area of science or engineering with complex systems, such as biology.

The method allows scientists to study molecular interactions in a human cell, for example, which ordinarily creates considerable computing challenges, says Ben Brown of Berkeley Lab's Environmental Genomics and Systems Biology Division, one of the paper's lead senior authors.

The scientists demonstrated their method on two genomics problems, and they are now using it to design phased array laser systems and to optimize sustainable agriculture systems.

From Berkeley Lab News Center
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