Neurology

Autism

Latest AI and machine learning research in autism for healthcare professionals.

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The DREAM Dataset: Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy.

We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Dis...

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. Swi...

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 adi...

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data.

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential...

Artificial intelligence powered statistical genetics in biobanks.

Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens ...

Characterization of Spray Dried Particles Through Microstructural Imaging.

Spray drying is commonly used to produce amorphous solid dispersions (ASD) to improve the bioperform...

Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare.

Agent-based approaches have been known to be appropriate as systems and methods in medical administr...

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....

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. Mor...

Pan-cancer image-based detection of clinically actionable genetic alterations.

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environm...

Robot applications for autism: a comprehensive review.

PURPOSE: Technological advances in robotics have brought about exciting developments in different ar...

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, i...

Strategies for Testing Intervention Matching Schemes in Cancer.

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often...

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 biolog...

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,...

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 n...

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. ...

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