Neurology

Autism

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

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Multi-modal medical image classification using deep residual network and genetic algorithm.

Artificial intelligence (AI) development across the health sector has recently been the most crucial...

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases.

BACKGROUND: The diagnosis of rare genetic diseases is often challenging due to the complexity of the...

DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification.

BACKGROUND: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterize...

Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis.

The current practices of designing neural networks rely heavily on subjective judgment and heuristic...

Active Learning Optimisation of Binary Coded Metasurface Consisting of Wideband Meta-Atoms.

The design of a metasurface array consisting of different unit cells with the objective of minimizin...

HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displaye...

Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility.

Sperm motility is crucial for successful fertilization. Highly decorated doublet microtubules (DMTs)...

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.

BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial ...

Design Path for a Social Robot for Emotional Communication for Children with Autism Spectrum Disorder (ASD).

Children with autism spectrum disorder (ASD) have deficits in social interaction and expressing and ...

Three levels of information processing in the brain.

Information, the measure of order in a complex system, is the opposite of entropy, the measure of ch...

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning.

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 mil...

The future of commercial genetic testing.

PURPOSE OF REVIEW: There are thousands of different clinical genetic tests currently available. Gene...

SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data.

BACKGROUND: Structural variations (SVs) refer to variations in an organism's chromosome structure th...

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions.

Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and ...

Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine.

The single largest contributor to human mortality is cardiovascular disease, the top risk factor for...

Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: A case study in autism.

Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when ev...

Motor differences in autism during a human-robot imitative gesturing task.

BACKGROUND: Difficulty with imitative gesturing is frequently observed as a clinical feature of auti...

A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images.

Evolutionary algorithms have been successfully employed to find the best structure for many learning...

Development and Validation of a Joint Attention-Based Deep Learning System for Detection and Symptom Severity Assessment of Autism Spectrum Disorder.

IMPORTANCE: Joint attention, composed of complex behaviors, is an early-emerging social function tha...

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning.

Protein-coding differences between species often fail to explain phenotypic diversity, suggesting th...

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