Neurology

Autism

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

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A novel framework based on explainable AI and genetic algorithms for designing neurological medicines.

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its...

RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.

Microbial rhodopsins (MRs) are a diverse and abundant family of photoactive membrane proteins that s...

Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling.

Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In...

Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.

The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and thera...

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given th...

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pre...

Predicting autism traits from baby wellness records: A machine learning approach.

Timely identification of autism spectrum conditions is a necessity to enable children to receive the...

Development and Validation of Prediction Models for the Diagnosis of Autism Spectrum Disorder in a Korean General Population.

OBJECTIVE: Delays in autism spectrum disorder (ASD) diagnosis and treatment are significant clinical...

Ancestry analysis using a self-developed 56 AIM-InDel loci and machine learning methods.

Insertion/deletion (InDel) polymorphisms can be used as one of the ancestry-informative markers in a...

Stacked neural network for predicting polygenic risk score.

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility fr...

Deep learning of left atrial structure and function provides link to atrial fibrillation risk.

Increased left atrial volume and decreased left atrial function have long been associated with atria...

Residual networks without pooling layers improve the accuracy of genomic predictions.

Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its pred...

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating is...

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating p...

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different e...

ADHD classification with cross-dataset feature selection for biomarker consistency detection.

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in childr...

Performance Evaluation of a Supervised Machine Learning Pain Classification Model Developed by Neonatal Nurses.

BACKGROUND: Early-life pain is associated with adverse neurodevelopmental consequences; and current ...

A hybrid CNN-SVM model for enhanced autism diagnosis.

Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an...

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