Autism

Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics.

Following prolonged swimming, cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for and here we suggest that inactive bouts are a recovery state akin to fatigue. It is known that cGMP-dependent kinase (PKG) activity plays a conserved role in sleep, rest, and arousal. Using EGL-4 PKG, we first validate a novel learning-based computer vision approach to automa...

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the fi...

Artificial intelligence powered statistical genetics in biobanks.

Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens such as blood, urine and tissues, have been establ...

Characterization of Spray Dried Particles Through Microstructural Imaging.

Spray drying is commonly used to produce amorphous solid dispersions (ASD) to improve the bioperformance of poorly water-soluble drugs. In this study,...

Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration.

Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues ...

Self-initiations in young children with autism during Pivotal Response Treatment with and without robot assistance.

The initiation of social interaction is often defined as a core deficit of autism spectrum disorder. Optimizing these self-initiations is therefore a ...

In-plane gait planning for earthworm-like metameric robots using genetic algorithm.

Locomotion of earthworm-like metameric robots results from shape changes of deformable segments. Morphologically, the segments could stretch, contract...

Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on s...

Robot applications for autism: a comprehensive review.

PURPOSE: Technological advances in robotics have brought about exciting developments in different areas such as education, training, and therapy. Rece...

Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.

Autism spectrum disorders (ASDs) are heterogeneous neurodevelopmental conditions. In fMRI studies, including most machine learning studies seeking to ...

Strategies for Testing Intervention Matching Schemes in Cancer.

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behav...

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. Howev...

EAGA-MLP-An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis.

Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in comp...

Feed-forward neural networks using cerebral MR spectroscopy and DTI might predict neurodevelopmental outcome in preterm neonates.

OBJECTIVES: We aimed to evaluate the ability of feed-forward neural networks (fNNs) to predict the neurodevelopmental outcome (NDO) of very preterm ne...

A deep learning model for detecting mental illness from user content on social media.

Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identif...

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemora...

Convolutional neural networks and genetic algorithm for visual imagery classification.

Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the ...

Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

RATIONALE: Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patie...

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a co...

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering.

The objective is to improve the prediction accuracy of foundation pit deformation in geotechnical engineering, thereby provide early warning for engin...