Like a game of Wheel of Fortune, where you have to fill in missing letters to guess the hidden phrase, analyzing data sometimes requires estimating missing data points by relying on available information in order to get the full picture of what’s being studied.
Machine Learning
New Machine Learning Tool Helps Researchers Demonstrate Effectiveness of Stem Cell Based Models
Today, many researchers are using brain organoids – miniaturized and simplified versions of organs produced in a dish typically from stem cells – as analogs for studying the development of the human brain.
Researchers are using machine learning to understand how brain cells work
For something so small, neurons can be quite complex — not only because there are billions of them in a brain, but because their function can be influenced by many factors, like their shape and genetic makeup.
Artificial intelligence can accelerate clinical diagnosis of fragile X syndrome
An analysis of electronic health records for 1.7 million Wisconsin patients revealed a variety of health problems newly associated with fragile X syndrome.
New researcher uses machine learning to decode genomic information
Recent advances in genome sciences — the study of an organism’s complete set of DNA — present a golden opportunity to identify the genetic causes and underlying mechanisms of intellectual and developmental disabilities. These discoveries …
Andrew Alexander, PhD – Slide of the Week
The segmentation of small brain structures like the amygdala is quite challenging in the presence of distorted and abnormal anatomy from major brain injuries.
Electronic records pin broad set of health risks on genetic premutation
It was long believed the FMR1 premutation — an excessive number of trinucleotide repeats in the FMR1 gene — had no direct effect on the people who carry it. Until recently, the only recognized effect on the carriers of the flawed gene was the risk of having offspring with fragile X syndrome, a rare but serious form of developmental disability.
Lauren Bishop-Fitzpatrick, PhD – Slide of the Week
As a large wave of individuals with autism spectrum disorder (ASD) diagnosed in the 1990s enters adulthood and middle age, knowledge about the patterning of lifetime health problems will become increasingly important for prevention efforts. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a machine learning algorithm to characterize diagnostic patterns in decedents with ASD and matched decedent community controls.
Using artificial intelligence for a big impact on neurodevelopmental research
Arezoo Movaghar earned her master’s degree in computer science and artificial intelligence. She built models based on the plentiful data found in medical records. So, when she came to UW–Madison as a PhD student and joined a research group, it surprised Movaghar to find out just how much data researchers in other fields collect.
Scientists just beginning to understand the unique health needs of adults with autism
In the 1990s, the prevalence of autism spectrum disorder (ASD) among children rose sharply. These children are now entering adulthood, yet physicians and scientists know very little about the health outcomes they might face. Most studies of health have focused on children and adolescents.