Autism

Progress on deep learning in genomics.

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning-an advanced artificial intelligence technology-offers powerful capabilities for data ...

Role of Data-driven Regional Growth Model in Shaping Brain Folding Patterns

The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction. Computational modeling offers valuable insights into the underlying mechanisms for early brain folding. Recent findings indicate significant regional variations in brain tissue growth, while the role of these variations in cortical development remains unclear. In this study, we u...

Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework

Autism Spectrum Disorder (ASD) is a rapidly growing neurodevelopmental disorder. Performing a timely intervention is crucial for the growth of young...

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain in-utero. Recent studies have shown that dMR...

Including Non-Autistic Peers in Games Designed for Autistic Socialization

Through a review of current game practices, the author highlights concerns regarding the safety of public social games and the singular medical appr...

Screen Them All: High-Throughput Pan-Cancer Genetic and Phenotypic Biomarker Screening from H&E Whole Slide Images

Many molecular alterations serve as clinically prognostic or therapy-predictive biomarkers, typically detected using single or multi-gene molecular ...

Predicting the genetic component of gene expression using gene regulatory networks

Gene expression prediction plays a vital role in transcriptome-wide association studies (TWAS), which seek to establish associations between tissue ...

Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors

The intersection of artificial intelligence and bioinformatics has enabled significant advancements in drug discovery, particularly through the appl...

Anatomical Foundation Models for Brain MRIs

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of th...

Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data.

BACKGROUND: Hemoglobinopathies, the most common inherited blood disorder, are frequently underdiagnosed. Early identification of carriers is important...

A novel sand cat swarm optimization algorithm-based SVM for diagnosis imaging genomics in Alzheimer's disease.

In recent years, brain imaging genomics has advanced significantly in revealing underlying pathological mechanisms of Alzheimer's disease (AD) and pro...

Interpreting artificial neural networks to detect genome-wide association signals for complex traits

Investigating the genetic architecture of complex diseases is challenging due to the multifactorial and interactive landscape of genomic and environ...

A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.

Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. Howeve...

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory p...

GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning

Genetic variants (GVs) are defined as differences in the DNA sequences among individuals and play a crucial role in diagnosing and treating genetic ...

Applications of Artificial Intelligence in Psychiatric Nursing: A Scope Review.

Rapid advances in artificial intelligence (AI) have reshaped healthcare, including psychiatric nursing, to address the limitations of traditional appr...

Visual Stereotypes of Autism Spectrum in DALL-E, Stable Diffusion, SDXL, and Midjourney

Avoiding systemic discrimination requires investigating AI models' potential to propagate stereotypes resulting from the inherent biases of training...

Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variabilit...

FastImpute: A Baseline for Open-source, Reference-Free Genotype Imputation Methods -- A Case Study in PRS313

Genotype imputation enhances genetic data by predicting missing SNPs using reference haplotype information. Traditional methods leverage linkage dis...

Dual Attention Graph Convolutional Network Fusing Imaging and Genetic Data for Early Alzheimer's Disease Diagnosis.

Alzheimer's Disease (AD) poses a significant global neurodegenerative challenge, underscoring the urgency of early clinical intervention. Our paper pr...