Autism

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. While several biomarkers have been proposed, comprehensive immunological analyses integrating mass cytometry (CyTOF) and T-cell receptor sequencing (TCRseq) data remain limited. In this study, we applied the Latent Class Model based on the Bayesian Information Criterion (LCM-BIC) algorithm to integ...

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 autism spectrum disorder (ASD) treatment. A large amount of research literature shows that family intervention is effective in ASD rehabilitation. Still, the concept of family intervention is vaguely defined, and there is a lack of descriptions of specific intervention programs and treatment pathwa...

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 genetic variants and disease traits to date. Yet, ide...

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 applying machine learning techniques. A cross-section...

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

BACKGROUND: Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect n...

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 distribution of food contaminated by bacteria. Thi...

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 cell performance to create more powerful cancer imm...

Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.

Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior....

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 mortality. Technological advances have increased therape...

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 disorder (ASD) diagnosis. Nonetheless, owing to the amb...

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) and typically developing (TD) individuals based on ...

[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 cause of blindness in older adults worldwide. Accor...

A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnos...

STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis

Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and att...

[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 advanced registration models having been proposed, achie...

A CNN Approach to Polygenic Risk Prediction of Kidney Stone Formation

Kidney stones are a common and debilitating health issue, and genetic factors play a crucial role in determining susceptibility. While Genome-Wide A...

AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals

Generative AI has the potential to transform knowledge work, but further research is needed to understand how knowledge workers envision using and i...

High-throughput digital twin framework for predicting neurite deterioration using MetaFormer attention

Neurodevelopmental disorders (NDDs) cover a variety of conditions, including autism spectrum disorder, attention-deficit/hyperactivity disorder, and...

GREGoR: Accelerating Genomics for Rare Diseases

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in...

Analytical-Heuristic Modeling and Optimization for Low-Light Image Enhancement

Low-light image enhancement remains an open problem, and the new wave of artificial intelligence is at the center of this problem. This work describ...