Goto

Collaborating Authors

Machine Learning Is Helping Us Find The Genetics Of Autism

#artificialintelligence

The genetic cause of autism spectrum disorder is notoriously hard to research. Genetic markers for the disorder are tough to match from patient to patient because they're so rare--one of the most common genetic signifiers is only found in less than one percent of those diagnosed with autism. Even when genetic anomalies are found, they must be checked against family members genomes to ensure it's not attributable to a more commonly inherited mutation that doesn't cause disease. 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.


Outsmarting Disease -- With Artificial Intelligence

#artificialintelligence

More and more, 67-year-old Washington resident Lon Coleman feels like he's wandering through a fog. He walks into the living room and forgets why, or makes a phone call only to blank on whose number he dialed. An author of three books who once wrote up to five poems a day, now the lines that spring to his mind often slip away as soon as he puts pencil to paper. Sometimes the fog clears, and when his memory comes back, "it's amazing," he says. "Sometimes it doesn't, I have to admit."


Thanks to AI, Computers Can Now See Your Health Problems

WIRED

Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.


IBM, Pfizer form research pact to tackle Parkinson's Disease

ZDNet

IBM Research Data Scientist Eric Clark explores wearable technologies that could help monitor and analyze biological data from study subjects on Thursday, April 7, 2016 at IBM's T. J. Watson Research Center in Yorktown, NY. IBM and Pfizer are teaming up in an effort to give Parkinson's Disease research an analytical edge. The tech titan and the pharmaceutical giant plan to utilize non-invasive wearables to collect and monitor real-time patient data. The end goal of the research project is to advance the way neurological diseases are diagnosed and treated while also speeding up clinical trials to bring new drugs to market. The study, which is expected to last up to three years, will glean patient data from a system of sensors, wearables and mobile devices that will monitor patients around the clock, not just episodically.


Researchers develop machine-learning program that helps identify hundreds of ASD-related genes

#artificialintelligence

Investigators eager to uncover the genetic basis of autism could now have hundreds of promising new leads thanks to a study by Princeton University and Simons Foundation researchers. In the first effort of its kind, the research team developed a machine-learning program that scoured the whole human genome to predict which genes may contribute to autism spectrum disorder (ASD). The results of the program's analyses -- a rogue's gallery of 2,500 candidate genes -- vastly expand on the 65 autism-risk genes currently known. Researchers have recently estimated that 400 to 1,000 genes underpin the complex neurodevelopmental disorder. This newest research provides a manageable, "highly enriched" pool from which to pin down the full suite of ASD-related genes, the researchers said.