A new app developed at the Waisman Center makes it easier than ever for researchers to use machine learning techniques to analyze large complex data sets without specialized or specific training.
A new study from researchers at the Waisman Center and The Ohio State University will investigate aging in autistic adults.
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.
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.
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.
An analysis of electronic health records for 1.7 million Wisconsin patients revealed a variety of health problems newly associated with fragile X syndrome.
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 …
The segmentation of small brain structures like the amygdala is quite challenging in the presence of distorted and abnormal anatomy from major brain injuries.
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.
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.