Machine learning helps uncover genetics of autism

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.

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…

Moving forward, the team is looking at how this same technique could be applied for mapping individual patient genomes.

The GLP aggregated and excerpted this blog/article to reflect the diversity of news, opinion and analysis. Read full, original post: Machine Learning is Helping Us Find the Genetics of Autism

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