Autism

Excitatory Dysfunction and Phenotypic Rescue in a Human Neuronal Model of SCN2A-Related Disorders

SCN2A-related disorders result from pathogenic variants in the gene encoding for the voltage-gated sodium channel Nav1.2. Collectively, these disorders result in variable age of onset epilepsy, autism spectrum disorder, and epileptic encephalopathies. While the mechanisms of haploinsufficiency resulting in autism spectrum disorder have been explored in detail, few studies report the impact of path...

Von Economo neurons enable reliable social skill acquisition in recurrent spiking neural networks: a computational account with clinical predictions

Von Economo neurons (VENs) are selectively lost in behavioural-variant frontotemporal dementia (bvFTD) and reduced in autism spectrum conditions (ASC), yet their computational role in social learning remains unexplained. We train a spiking neural network (the VENCircuit) embedding VEN-like projection neurons (K=40, 2% of total) in a recurrent pyramidal circuit across 50 matched random initialisati...

Advancing diagnosis of bipolar disorder using brain morphometric similarity networks in a graph AI framework

Brain similarity networks (BSNs), extracted from structural magnetic resonance imaging, provide a validated framework for studying brain network organ...

CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data

Copy number variations (CNVs) are major structural genomic variants that contribute to a wide range of human diseases. Accurate detection of CNVs from...

Autobehaver: An AI-Based Pipeline for Animal Behavior Analysis

Behavior arises from the complex interplay between the nervous system, genetics, and the environment. High-resolution, high-throughput behavioral quan...

Using Deep Learning Models of Gene Regulation to Guide Drug Prioritization

Drug repurposing offers a cost-effective strategy to accelerate therapeutic discovery, but most computational approaches fail to model noncoding genet...

DynoSys 2.0: Graph-Based Modeling of Dynamic Risk States and System Transitions in Human Behaviours Development

Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks oft...

Swarm-GestaltMatcher: distributed Gestalt learning through Swarm Learning to enhance facial phenotyping for rare genetic syndromes

Deep learning-based facial phenotyping represents a major paradigm shift in the diagnosis of rare and ultra-rare genetic disorders. By capturing disea...

Transferable Transcriptional Topic Modeling Traces Medulloblastoma Subtypes to Distinct Cerebellar Developmental States

Single-cell transcriptomics transformed our understanding of cellular heterogeneity, yet cross-dataset comparison remains fundamentally limited by bat...

Systematic toxicological study of PFOS/PFOA co-exposure driving prostate cancer: Core target identification, TME immune remodeling, and combination drug prediction

Background: Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), are persisten...

Integrative taxonomy using traits and genomic data for Species Delimitation with Deep learning

Recognizing species boundaries in complex speciation scenarios, including those involving gene flow and demographic fluctuations, remains a challenge,...

An Explainable Deep Learning Framework for Imaging Genetics: Deriving Brain-Genotype Scores From MRI to Link Genetic Variation, Brain Structure, and Cognition

Imaging genetics aims to understand how genetic variation influences brain structure and cognitive function. Traditional approaches often rely on imag...

Early Detection of Rare Disease Using Hierarchical Set-to-Sequence Modeling of Structured Electronic Health Records

Rare diseases are characterized by heterogeneous, weak, and sparse phenotypic signals that emerge gradually across longitudinal clinical visits, makin...

BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning

Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substa...

An Interpretable Deep Learning Framework Reveals Frontoparietal Control Network Hyperactivation Underlying Autism Diagnosis and Symptom Severity

BACKGROUND: Autism spectrum disorder (ASD) is marked by profound neurobiological heterogeneity, which drives inconsistent neuroimaging findings and im...

Scalable machine learning improves resistance prediction and identifies novel determinants in Mycobacterium tuberculosis

Multidrug-resistant and extensively drug-resistant Mycobacterium tuberculosis (MTB) represents a growing global health crisis, characterized by limite...

A Multi-modal LLM-Knowledge Fusion Framework for Predicting Single-cell Genetic Perturbation Effects

Understanding cellular responses to genetic perturbations is fundamental for drug discovery, yet experimental approaches face significant limitations ...

Learning dynamics of unsupervised deep learning reveal epoch-specific genetic architectures of brain morphology

Representation learning is an emerging paradigm for deriving phenotypes from complex measurements (e.g., imaging) for genetic discovery. However, the ...

Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive, rapid deterioration of motor neurons (MNs)...

SNPic: SNP Topic Modeling for Interpretable Clustering of Complex phenotypes

Genome-wide association studies (GWAS) have cataloged thousands of disease-associated variants, yet a central challenge remains: decoding the shared, ...