Neurology

Autism

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

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Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.

INTRODUCTION: IVF is a widely-used assisted reproductive technology with a consistent success rate o...

Building and testing of a robotic intervention framework to enhancing the social engagement of children with autism spectrum disorder.

PURPOSE: Humanoid robot intervention programmes for children with autism spectrum disorder (ASD) are...

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis ...

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms.

BACKGROUND AND OBJECTIVE: Fetal brain tissue segmentation provides foundational support for comprehe...

Video-audio neural network ensemble for comprehensive screening of autism spectrum disorder in young children.

A timely diagnosis of autism is paramount to allow early therapeutic intervention in preschoolers. D...

Prediction of Crohn's disease based on deep feature recognition.

BACKGROUND: Crohn's disease is a complex genetic disease that involves chronic gastrointestinal infl...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

The integration of different imaging modalities, such as structural, diffusion tensor, and functiona...

Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm.

Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patient...

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organi...

CNVoyant a machine learning framework for accurate and explainable copy number variant classification.

The precise classification of copy number variants (CNVs) presents a significant challenge in genomi...

Interpretable machine learning for allergic rhinitis prediction among preschool children in Urumqi, China.

This study aimed to investigate the advantages and applications of machine learning models in predic...

Creating a diagnostic assessment model for autism spectrum disorder by differentiating lexicogrammatical choices through machine learning.

This study explores the challenge of differentiating autism spectrum (AS) from non-AS conditions in ...

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers.

Curated online interaction databases and gene ontology tools have streamlined the analysis of highly...

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling.

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical...

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissu...

DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data.

Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring como...

Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.

One Health and planetary health place emphasis on the common molecular mechanisms that connect sever...

Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.

The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and de...

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