Saturday December 14, 2019

Artificial Intelligence Discovers New Class of Genetic Mutations Behind Autism

The newly found mutations are likely to significantly increase that fraction, the researchers said

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Using Artificial Intelligence (AI), researchers have discovered new genetic flaws that contribute to autism in people.

Most previous research on the genetic basis of disease has focused on the 20,000 known genes and the surrounding sections of DNA that regulate those genes.

However, even this enormous amount of genetic information makes up only slightly more than one per cent of the 3.2 billion chemical pairs in the human genome.

The other 99 per cent has conventionally been thought of as “dark” or “junk,” although recent research has begun to disrupt that idea.

In their new finding, detailed in the journal Nature Genetics, the research team offers a method to make sense of this vast array of genomic data.

The system uses an AI technique called deep learning in which an algorithm performs successive layers of analysis to learn about patterns that would otherwise be impossible to discern.

The algorithm teaches itself how to identify biologically relevant sections of DNA and predicts whether those snippets play a role in any of more than 2,000 protein interactions that are known to affect the regulation of genes.

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“Many people with autism also have a serious intestinal illness, like diarrhea and irritable-bowel syndrome. It is consistent with our findings,” Liu said. Pixabay

“This method provides a framework for doing this analysis with any disease,” said Olga Troyanskaya, Professor at Princeton University in the US.

The approach could be particularly helpful for neurological disorders, cancer, heart disease and many other conditions that have eluded efforts to identify genetic causes.

In the case of autism, the researchers analysed the genomes of 1,790 families with “simplex” autism spectrum disorder, meaning the condition is apparent in one child but not in other members of the family.

The method sorted among 120,000 mutations to find those that affect the behaviour of genes in people with autism.

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Among this sample, fewer than 30 per cent of the people affected by autism spectrum disorder had a previously identified genetic cause.

The newly found mutations are likely to significantly increase that fraction, the researchers said. (IANS)

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New AI can Reduce Risk of Suicide Among Youth

AI can help prevent suicide among youth

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Researchers from USC developed an AI that can prevent suicide risks among youth. Lifetime Stock

In a bid to help mitigate the risk of suicide especially among the homeless youth, a team of researchers at University of California (USC) has turned their focus towards Artificial Intelligence (AI).

Phebe Vayanos, an associate director at USC’s Center for Artificial Intelligence in Society (CAIS), and her team have been working over the last couple of years to design an algorithm capable of identifying who in a given real-life social group would be the best persons to be trained as “gatekeepers” capable of identifying warning signs of suicide and how to respond.

“Our idea was to leverage real-life social network information to build a support network of strategically positioned individuals that can ‘watch-out’ for their friends and refer them to help as needed,” Vayanos said.

Vayanos and study’s lead author Aida Rahmattalabi investigated the potential of social connections such as friends, relatives and acquaintances to help mitigate the risk of suicide.

“We want to ensure that a maximum number of people are being watched out for, taking into account resource limitations and uncertainties of open world deployment,” Vayanos said.

Youth suicide
The AI algorithm can improve the efficiency of suicide prevention trainings. Lifetime Stock

For this study, Vayanos and Rahmattalabi looked at the web of social relationships of young people experiencing homelessness in Los Angeles, given that 1 in 2 youth who are homeless have considered suicide.

“Our algorithm can improve the efficiency of suicide prevention trainings for this particularly vulnerable population,” Vayanos said.

An important goal when deploying this AI system is to ensure fairness and transparency.

“This algorithm can help us find a subset of people in a social network that gives us the best chance that youth will be connected to someone who has been trained when dealing with resource constraints and other uncertainties,” said study co-author Anthony Fulginiti.

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This work is particularly important for vulnerable populations, say the researchers, particularly for youth who are experiencing homelessness.

The paper is set to be presented at the 33rd Conference on Neural Information Processing Systems (NeurIPS) in Vancouver, Canada, this month. (IANS)