Autism

Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis

Developing interpretable models for diagnosing neurodevelopmental disorders (NDDs) is highly valuable yet challenging, primarily due to the complexity of encoding, decoding and integrating imaging and non-imaging data. Many existing machine learning models struggle to provide comprehensive interpretability, often failing to extract meaningful biomarkers from imaging data, such as functional magn...

Efficient 4D fMRI ASD Classification using Spatial-Temporal-Omics-based Learning Framework

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder impacting social and behavioral development. Resting-state fMRI, a non-invasive tool for capturing brain connectivity patterns, aids in early ASD diagnosis and differentiation from typical controls (TC). However, previous methods, which rely on either mean time series or full 4D data, are limited by a lack of spatial information or ...

Unsupervised Clustering Approaches for Autism Screening: Achieving 95.31% Accuracy with a Gaussian Mixture Model

Autism spectrum disorder (ASD) remains a challenging condition to diagnose effectively and promptly, despite global efforts in public health, clinic...

Giving AI Personalities Leads to More Human-Like Reasoning

In computational cognitive modeling, capturing the full spectrum of human judgment and decision-making processes, beyond just optimal behaviors, is ...

Genetic Data Governance in Crisis: Policy Recommendations for Safeguarding Privacy and Preventing Discrimination

Genetic data collection has become ubiquitous today. The ability to meaningfully interpret genetic data has motivated its widespread use, providing ...

Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic b...

KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical ...

Multi-Site rs-fMRI Domain Alignment for Autism Spectrum Disorder Auxiliary Diagnosis Based on Hyperbolic Space

Increasing the volume of training data can enable the auxiliary diagnostic algorithms for Autism Spectrum Disorder (ASD) to learn more accurate and ...

SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

$\textbf{Background}$: Cancer remains one of the leading causes of morbidity and mortality worldwide. Comprehensive datasets that combine histopatho...

Unravelling Causal Genetic Biomarkers of Alzheimer's Disease via Neuron to Gene-token Backtracking in Neural Architecture: A Groundbreaking Reverse-Gene-Finder Approach

Alzheimer's Disease (AD) affects over 55 million people globally, yet the key genetic contributors remain poorly understood. Leveraging recent advan...

Diagnosis of Alzheimer's disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data.

Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of Alzheimer's disease is beneficial for its prevention ...

The role of the dopamine system in autism spectrum disorder revealed using machine learning: an ABIDE database-based study.

This study explores the diagnostic value of dopamine system imaging characteristics in children with autism spectrum disorder. Functional magnetic res...

A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis

Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity networks (FCNs) have become critical for under...

FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI

Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reve...

BrainOOD: Out-of-distribution Generalizable Brain Network Analysis

In neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer's and Autism, is critical for early diagnosis and...

SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (...

DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits

DCentNet is a novel decentralized multistage signal classification approach designed for biomedical data from IoT wearable sensors, integrating earl...

Human-Precision Medicine Interaction: Public Perceptions of Polygenic Risk Score for Genetic Health Prediction

Precision Medicine (PM) transforms the traditional "one-drug-fits-all" paradigm by customising treatments based on individual characteristics, and i...

A network-driven framework for enhancing gene-disease association studies in coronary artery disease

Over the last decade, genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with complex diseases...

Planning with Vision-Language Models and a Use Case in Robot-Assisted Teaching

Automating the generation of Planning Domain Definition Language (PDDL) with Large Language Model (LLM) opens new research topic in AI planning, par...