Autism

A biologically informed hierarchical feature engineering and learning framework for genetic disorder prediction.

Genetic disorder prediction from diverse clinical and hereditary sources is very difficult due to complex inheritance patterns, high interdependencies among symptoms and high-dimensional mixed-type feature spaces. Traditional machine learning methods usually involve generic preprocessing pipelines and flat single-stage classification which do not effectively utilize the biological structure and hi...

3D machine learning-based complexity variability and fluidity quantification of preterm and writhing general movements.

General Movement Assessment (GMA) is a reliable non-invasive method for the early detection of neurodevelopmental disorders in infants, based on the qualitative evaluation of three key parameters: complexity, variability, and fluidity. Current automated approaches primarily use composite indices on two-dimensional data, limiting parameter-specific evaluations and failing to fully exploit three-dim...

Novel genetic insights into causal effects of depression on non-alcoholic fatty liver diseases partially mediated by gut microbiota.

Depression and non-alcoholic fatty liver disease (NAFLD) are increasingly recognized as interconnected disorders, yet the causal mechanisms linking th...

A bibliometric analysis of research trends and future directions in early detection of pancreatic cancer.

BACKGROUND: Research on the early detection of pancreatic cancer has grown rapidly in recent years; however, existing bibliometric studies in this fie...

Integrated remote sensing and aeromagnetic datasets for mapping iron mineralization potential in the El-Bahariya depression in the Western Desert of Egypt.

The El-Bahariya depression in the Western Desert of Egypt is well-known for its iron ore deposits, with mineralization recorded in five well-known loc...

Evaluation of an AI-based segmentation model for mandibular fracture segmentation on CBCT using volumetric and 2D slice-based validation approaches.

OBJECTIVE: This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based segmentation model for mandibular fracture...

A multi-scale, circuit-to-gene signature of approach-bias modification in internet gaming disorder.

BACKGROUND: Approach-bias modification (ApBM) is a cognitive training intervention with potential therapeutic value for internet gaming disorder (IGD)...

SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.

Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of...

A Color Preference Recognition System for Children with Autism Spectrum Disorder Based on Fully Flexible Triboelectric Sensors and Artificial Intelligence Technology.

Appropriate color intervention can effectively regulate the autonomic arousal level of children with autism, improve their attention, and mitigate the...

Marfan Syndrome Prediction Via Graph Neural Networks on 3D Facial Cues.

Marfan syndrome (MFS) is a rare genetic connective tissue disorder whose early detection is critical to prevent life-threatening cardiovascular compli...

AI-CURA, an automated LLM workflow for high-accuracy genetic variant classification.

Large language models (LLMs) have been extensively tested for incorporation into medical applications in recent years; however, their potential in cli...

Molecular characteristics and modifications of glioblastoma affecting the efficacy of immunotherapy.

Glioblastoma is a highly aggressive brain tumor characterized by complex genetic, molecular, and epigenetic features that present significant challeng...

Codon compression and novel codon creation for multiplex non-canonical amino acid incorporation.

Genetic code expansion (GCE) provides a robust platform for engineering protein function via the site-specific incorporation of noncanonical amino aci...

Metabolomic signatures of brain aging: A multimodal and genetic study.

Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its meta...

Deep learning-based identification of causative genes in lissencephaly using 3D-MRI volumetric datasets.

BACKGROUND: This paper reports a genetic identification task using 3D convolutional neural network (3D-CNN) models applied to a proprietary 3D magneti...

NeuroCypher ASD: Machine Learning with Knowledge Graph-Based Representation for ASD Screening in Toddlers.

Autism Spectrum Disorder (ASD) screening often relies on structured questionnaires. Yet these data are not always easy to interpret or use in practice...

Network analysis reveals that bHLH transcription factors positively regulate rice resistance to Magnaporthe oryzae.

Transcription factors (TFs) and nucleotide-binding leucine-rich repeat (NLR) genes are core components of the immune response in rice against Magnapor...

From the Brain Cell Atlas to Precision Neurology: A review of the application of AI-driven multi-omics in brain science.

Recent advances in multi-omics technologies have catalyzed the construction of comprehensive brain cell atlases, providing essential data foundations ...

Construction of a small-sample brain imaging data augmentation and explainable diagnostic model for autism based on generative adversarial networks.

BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social communication deficits and repetitive st...

Prediction of lower limb compensation for adult degenerative spine patients using spinopelvic parameters: a machine learning approach.

STUDY DESIGN: Retrospective study. OBJECTIVE: This work aims to estimate using machine learning the occurrence of knee flexion in relation to spinopel...