Autism

Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity

Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated. We propose RE-CONFIRM, a framework for evaluating the robustness of potenti...

Predictive Modeling of Natural Medicinal Compounds for Alzheimer Disease Using Cheminformatics

The most common cause of dementia is Alzheimer disease, a progressive neurodegenerative disorder affecting older adults that gradually impairs memory, cognition, and behavior. It is characterized by the accumulation of abnormal proteins in the brain, including amyloid-beta plaques and neurofibrillary tangles of tau protein, which disrupt neuronal communication and lead to neuronal death. Early man...

Enabling the prediction of phage receptor specificity from genome data

Predicting which receptor a phage binds to from genome sequence alone has remained an intractable challenge, principally because the experimental phen...

CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder

Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance...

Strain- and age-dependent divergence in mouse appetitive spatial learning and decision strategies

Animals rely on associative spatial memory to navigate toward previously learned, reward-associated goals. This reward-guided navigation is supported ...

Evolutionary-scale protein language models uncover beneficial variants in a Sorghum bicolor diversity panel

Quantitative genetic approaches such as genome-wide association studies and genomic prediction are widely used to identify favourable genetic variatio...

Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysis

Automated recognition of autistic behaviors in children is essential for early intervention and objective clinical assessment. However, the developmen...

Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework

The deep integration of communication with intelligence and sensing, as a defining vision of 6G, renders environment-aware channel prediction a key en...

Developmental brain age gap in prematurity and postnatally emerging delay in congenital heart disease

Brain development follows a precisely regulated biological timetable, with defined periods of vulnerability increasingly recognized in congenital diso...

Development and validation of an XGBoost model with SHAP-based interpretability and a web-based calculator for predicting extrauterine growth restriction in preterm infants

Background: Extrauterine growth restriction (EUGR) is a common and clinically significant complication among preterm infants, contributing to adverse ...

MAMGL: A memory-augmented meta-graph learning framework for adolescent major depression disorder diagnosis

Adolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain deve...

D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due ...

DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains cha...

A new iterative framework for simulation-based population genetic inference with improved coverage properties of confidence intervals

Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecula...

Age-related cerebellar genetic, neuronal and functional impairments are reversed by specific magnetic stimulation protocols

Age-related cognitive decline reflects progressive atrophic changes that advance through broad neural networks. There is no effective treatment. Howev...

Predicting Unseen Gene Perturbation Response Using Graph Neural Networks with Biological Priors

Predicting transcriptional responses to genetic perturbations is a central challenge in functional genomics. CRISPR Perturb-seq experiments measure ge...

WINDEX: A hierarchical integration of site- and window-based statistics for characterizing the footprint of positive selection in genome-wide population genetic data

Adaptive mutations, or mutations that confer a fitness benefit, can leave behind distinct signals in genetic data. Computational methods have improved...

Learning relationships in epidemiological data using graph neural networks

When designing control strategies for an infectious disease it is critical to identify the key pathways of transmission. Data on infected hosts - when...

Fully Automated Abstraction of Longitudinal Breast Oncology Records with Off-The-Shelf Large Language Models

Background: Manual chart abstraction is a major bottleneck in clinical research. In oncology, important outcomes such as disease recurrence and the tr...

Public attitudes toward sharing health data for artificial intelligence: Differences by data type and sector in the Health in Central Denmark cohort

Aims We aimed to examine public perceptions of sharing various types of health data relevant for AI development, including electronic health records, ...