Researchers at Princeton and the Simons Foundation turned the traditional approach on its head, teaching a machine learning algorithm to look for the genetic relationships that could cause autism. The algorithm scoured a digital network of the human genome’s interactions, looking for relationships and connections that are similar to those in previously-known markers for autism. The research shines a light on how the disorder hides within our genome, highlighting 2,500 genes ripe for further research.
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The results aren’t immediately useful for identifying the disorder in patients. Instead, they could make finding more autism-causing genes faster and less expensive. Now that scientists have a better idea of where to look, they can selectively sequence parts of the genome that correlate to the disorder.
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Machine learning algorithms, while able to comb through massive troves of data faster than humanly possible, lack human ability to learn with just a few examples…
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Moving forward, the team is looking at how this same technique could be applied for mapping individual patient genomes.
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