Autism

DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare Diseases

Identification of causal genes and pathways is a critical step for understanding the genetic underpinnings of rare diseases. We propose novel approaches to gene prioritization and pathway identification using DNA language model, graph neural networks, and genetic algorithm. Using HyenaDNA, a long-range genomic foundation model, we generated dynamic gene embeddings that reflect changes caused by ...

Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks

Divergent brain connectivity is thought to underlie the behavioral and cognitive symptoms observed in many neurodevelopmental disorders. Quantifying divergence from neurotypical connectivity patterns offers a promising pathway to inform diagnosis and therapeutic interventions. While advanced neuroimaging techniques, such as diffusion MRI (dMRI), have facilitated the mapping of brain's structural...

Advanced Gesture Recognition in Autism: Integrating YOLOv7, Video Augmentation and VideoMAE for Video Analysis

Deep learning and advancements in contactless sensors have significantly enhanced our ability to understand complex human activities in healthcare s...

Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders

The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning ...

HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks

Identifying unusual brain activity is a crucial task in neuroscience research, as it aids in the early detection of brain disorders. It is common to...

Long-range gene expression prediction with token alignment of large language model

Gene expression is a cellular process that plays a fundamental role in human phenotypical variations and diseases. Despite advances of deep learning...

Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models

Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally,...

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-dr...

DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation.

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization metho...

Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study.

Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the I...

MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.

The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the...

AIGen: an artificial intelligence software for complex genetic data analysis.

The recent development of artificial intelligence (AI) technology, especially the advance of deep neural network (DNN) technology, has revolutionized ...

Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph

We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates bra...

Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models

Brain imaging has allowed neuroscientists to analyze brain morphology in genetic and neurodevelopmental disorders, such as Down syndrome, pinpointin...

Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data

Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been shown to significantly improve the quality of life of autistic individu...

Exploring Gaze Pattern Differences Between Autistic and Neurotypical Children: Clustering, Visualisation, and Prediction

Autism Spectrum Disorder (ASD) affects children's social and communication abilities, with eye-tracking widely used to identify atypical gaze patter...

Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis

Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs)...

Gene and RNA Editing: Methods, Enabling Technologies, Applications, and Future Directions

Gene and RNA editing methods, technologies, and applications are emerging as innovative forms of therapy and medicine, offering more efficient imple...

Video-based Analysis Reveals Atypical Social Gaze in People with Autism Spectrum Disorder

In this study, we present a quantitative and comprehensive analysis of social gaze in people with autism spectrum disorder (ASD). Diverging from tra...

Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).

Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy using MRI ...