Neurology

Autism

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

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The role of the dopamine system in autism spectrum disorder revealed using machine learning: an ABIDE database-based study.

This study explores the diagnostic value of dopamine system imaging characteristics in children with...

Diagnosis of Alzheimer's disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data.

Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of Alz...

Integration of T cell repertoire, CyTOF, genotyping and symptomatology data reveals subphenotypic variability in COVID-19 patients.

COVID-19 manifests a broad spectrum of clinical outcomes, from asymptomatic cases to severe disease....

Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural network.

Genome-wide association studies (GWAS) have successfully uncovered numerous associations between gen...

A bibliometric analysis of the current state of research on family interventions for ASD.

UNLABELLED: In recent years, family intervention has become a hot research direction in the field of...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by app...

[Artificial intelligence in assessment of individual risks of age-related macular degeneration progression].

Age-related macular degeneration (AMD) is a progressive degenerative retinal disease and a leading c...

mGNN-bw: Multi-Scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis.

In recent years, computationally assisted diagnosis for classifying autism spectrum disorder (ASD) a...

Improving fMRI-Based Autism Severity Identification via Brain Network Distance and Adaptive Label Distribution Learning.

Machine learning methodologies have been profoundly researched in the realm of autism spectrum disor...

The Metabolic Treatabolome and Inborn Errors of Metabolism Knowledgebase therapy tool: Do not miss the opportunity to treat!

Inborn errors of metabolism (IEMs) are rare genetic conditions with significant morbidity and mortal...

Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.

Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differenc...

Understanding TCR T cell knockout behavior using interpretable machine learning.

Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T c...

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the ...

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network.

BACKGROUND: Brain variations are responsible for developmental impairments, including autism spectru...

[The dual-stream feature pyramid network based on Mamba and convolution for brain magnetic resonance image registration].

Deformable image registration plays a crucial role in medical image analysis. Despite various advanc...

[LORENZO'S OIL AND ADRENOLEUKODYSTROPHY EXAMINING AN ARTIFICIAL INTELLIGENCE TOOL INTENDED FOR CONDUCTING LITERATURE SEARCHES AND ANALYSES].

Adrenoleukodystrophy is a genetic metabolic disorder characterized by a heterogeneous phenotype. Its...

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised ...

KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for...

Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection.

The selection of biomarker panels in omics data, challenged by numerous molecular features and limit...

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