Autism

Generative artificial intelligence, integrative bioinformatics, and single-cell analysis reveal Alzheimer's genetic and immune landscape.

The research aims to understand Alzheimer's genetic and immune landscapes using the amalgamation of three technologies: artificial intelligence (GenAI), integrative bioinformatics, and single-cell analysis. First, the study aims to identify and characterize the significant genes associated with Alzheimer's disease (AD) using three GenAI models (GPT‑4o, Gemini model, and DeepSeek). After the genes ...

A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection.

Autism Spectrum Disorder (ASD) affects approximately [Formula: see text] of the global population and is characterized by difficulties in social communication and repetitive or obsessive behaviors. Early detection of autism is crucial, as it allows therapeutic interventions to be initiated earlier, significantly increasing the effectiveness of treatments. However, diagnosing ASD remains a challeng...

A deep learning-based ensemble for autism spectrum disorder diagnosis using facial images.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder leading to an inability to socially communicate and in extreme cases individuals are c...

Intersecting impact of CAG repeat and huntingtin knockout in stem cell-derived cortical neurons.

Huntington's Disease (HD) is caused by a CAG repeat expansion in the gene encoding huntingtin (HTT). While normal HTT function appears impacted by the...

Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder.

Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental factors, with unclear pathogenesis. This study aims t...

A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets.

This study explores the performance of deep learning models, specifically Convolutional Neural Networks (CNN) and XGBoost, in predicting alpha and bet...

Understanding Robot Gesture Perception in Children with Autism Spectrum Disorder during Human-Robot Interaction.

Social robots are increasingly being used in therapeutic contexts, especially as a complement in the therapy of children with Autism Spectrum Disorder...

Designing Clinical Trials for Patients With Rare Cancers: Connecting the Zebras.

The field of rare cancer research is rapidly transforming, marked by significant progress in clinical trials and treatment strategies. Rare cancers, a...

Machine Learning and Structural Dynamics-Based Approach to Reveal Molecular Mechanism of PTEN Missense Mutations Shared by Cancer and Autism Spectrum Disorder.

Missense mutations in oncogenic proteins that are concurrently associated with neurodevelopmental disorders have garnered significant attention. Phosp...

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently availa...

The differential diagnosis of autism spectrum disorder in adults.

INTRODUCTION: Diagnosing autism spectrum disorder (ASD) in adults is challenging due to its heterogeneity and symptom overlap with other conditions. M...

Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.

Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in m...

Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children.

Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and c...

FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI.

Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal ...

Leveraging artificial intelligence for diagnosis of children autism through facial expressions.

The global population contains a substantial number of individuals who experience autism spectrum disorder, thus requiring immediate identification to...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder ...

Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey.

Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to i...

Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.

Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to its diverse clinical manifestations and underlying...

Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.

OBJECTIVE: Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conven...

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.

The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI t...