Princeton University Scientists Use AI To Create Hierarchy of Gene Mutations That May Be Contributing to Autism

Building on what I had been pontificating and sharing on AI and autism-related research, I came across this break through from Princeton University. According to the report on the study, ” The method sorted among 120,000 mutations to find those that affect the behavior of genes in people with autism. Although the results do not reveal exact causes of cases of autism, they reveal thousands of possible contributors for researchers to study. ”

Here’s why and how AI saved thousands and thousands of dollars of research budget, not to mention precious time to arrive at robust findings:

“Prior to this computational achievement, the conventional way to glean such information would be painstaking laboratory experiments on each sequence and each possible mutation in that sequence. This number of possible functions and mutations is too big to contemplate — an experimental approach would require testing each mutation against more than 2,000 types of protein interactions and repeating those experiments over and over across tissues and cell types, amounting to hundreds of millions of experiments. Other research groups have sought to accelerate this discovery by applying machine learning to targeted sections of DNA, but had not achieved the ability to look at each DNA unit and each possible mutation and the effects on each of more than 2,000 regulatory interactions across the whole genome.

“What our paper really allows you to do is take all those possibilities and rank them,” said Park. “That prioritization itself is very useful, because now you can also go ahead and do the experiments in just the highest priority cases.”

Lastly, the system calibrates its predictions based on known disease-causing mutations and develops a “disease impact score,” an assessment of how likely a given mutation is to have an effect on disease.”

The sample of families who participated in the study was under n=1,800. It’s not too large, but strong enough for analytics. It goes to show us the efficacy of the method as well. The announcement also notes that the same method could be applied to cancer research and heart disease as well. This is an incredible step forward for humanity.

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