Autism

Recognition of autism in subcortical brain volumetric images using autoencoding-based region selection method and Siamese Convolutional Neural Network.

BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interactions and behavior. Accurate and early diagnosis of ASD is still challenging even with the improvements in neuroimaging technology and machine learning algorithms. It's challenging because of the wide range of symptoms, delayed appearance of symptoms, and the subjective nature of diagnosis. In t...

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here...

Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disord...

Pilot study protocol evaluating the impact of telerobotics interactions with autistic children during a Denver intervention on communication skills using single-case experimental design.

INTRODUCTION: For several years, studies have been conducted on the contribution of social robots as an intervention tool for children with autism spe...

A deep learning model of dorsal and ventral visual streams for DVSD.

Artificial intelligence (AI) methods attempt to simulate the behavior and the neural activity of the brain. In particular, Convolutional Neural Networ...

Height prediction of individuals with osteogenesis imperfecta by machine learning.

BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significan...

An explainable deep learning model to predict partial anomalous pulmonary venous connection for patients with atrial septal defect.

BACKGROUND: Patients with partial anomalous pulmonary venous connection (PAPVC) usually present asymptomatic and accompanied by intricate anatomical t...

A deep learning model for prediction of autism status using whole-exome sequencing data.

Autism is a developmental disability. Research demonstrated that children with autism benefit from early diagnosis and early intervention. Genetic fac...

Machine-learning based prediction of future outcome using multimodal MRI during early childhood.

The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays t...

Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet neural network.

In order to avoid damaging viscera during poultry evisceration and enhance the economic value of poultry products, this paper proposes a predictive me...

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation o...

A General DNA-Like Hybrid Symbiosis Framework: An EEG Cognitive Recognition Method.

In electroencephalogram (EEG) cognitive recognition research, the combined use of artificial neural networks (ANNs) and spiking neural networks (SNNs)...

Off-Body Sleep Analysis for Predicting Adverse Behavior in Individuals With Autism Spectrum Disorder.

Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a...

Machine learning model identifies genetic predictors of cisplatin-induced ototoxicity in CERS6 and TLR4.

BACKGROUND: Cisplatin-induced ototoxicity remains a significant concern in pediatric cancer treatment due to its permanent impact on quality of life. ...

Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods.

BACKGROUND AND AIMS: Autism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigatio...

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity.

Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders...

Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.

BACKGROUND: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimic...

AI-Based solutions for current challenges in regenerative medicine.

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of ta...

Key genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study.

Pancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, undersco...

Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in func...