Autism

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

BACKGROUND: Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its ...

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data.

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions by leveraging the power of multivariate analyses of large datasets. While deep learning (DL) offers another promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address thes...

Phylogenomics and phylogeographic model testing using convolutional neural networks reveal a history of recent admixture in the Canarian Kleinia neriifolia.

Multiple-island endemics (MIE) are considered ideal natural subjects to study patterns of island colonization that involve recent population-level gen...

Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm.

It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are n...

Investigating the impact of an AI-based play activities intervention on the quality of life of school-aged children with ADHD.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that not only impacts children's behavior, lear...

Graph based recurrent network for context specific synthetic lethality prediction.

The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more ...

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 being developed rapidly. This study aimed to deve...

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 and neurodevelopment within the central nervous sy...

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 comprehensively understanding the neurodevelopment of norm...

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. Deep Learning tools have been increasingly used to ...

Comparison of Patients With and Without Predicted Surgical Indication Between Clusters of Adult Spine Deformity (ASD) Patients.

STUDY DESIGN: Multicentric, retrospective analysis of prospectively collected data.

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

BACKGROUND: Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic...

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 functional magnetic resonance imaging, with deep learning m...

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 patients. Given this, early intervention is particularly ...

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 genomic medicine, primarily due to the complex nature of...

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

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimagin...

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 predicting the risk of allergic rhinitis (AR) in childre...

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 adolescents and adults, particularly considering t...

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods.

RATIONALE AND OBJECTIVES: To explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial i...

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 practice due to its complex etiology and progress...