Autism

Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable Artificial Intelligence.

Simplified detection of genetic background admixture using artificial intelligence.

Admixture refers to the mixing of genetic ancestry from different populations. Admixture is importan...

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disor...

Graph Node Classification to Predict Autism Risk in Genes.

This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph ne...

Electrophysiological and Visual Parameter Changes in Retinitis Pigmentosa Patients undergoing Autologous Platelet-Rich Plasma Therapy: A Randomized Control Trial.

PURPOSE: To assess the efficacy of autologous platelet-rich plasma (PRP) injections in suprachoroida...

Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management.

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of ...

Early diagnosis of children with autism using artificial intelligence during dental care.

Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network.

Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, ...

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To a...

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertai...

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants.

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively r...

Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning.

Robust quantification of vegetative biomass using satellite imagery using one or more forms of machi...

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magne...

A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. How...

Recognition of Genetic Conditions After Learning With Images Created Using Generative Artificial Intelligence.

IMPORTANCE: The lack of standardized genetics training in pediatrics residencies, along with a short...

Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides.

INTRODUCTION: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, po...

Artificial replication cohort: Leveraging AI-fabricated data for genetic studies.

Recent advancements in artificial intelligence (AI) present both opportunities and challenges within...

Comparison of clinical geneticist and computer visual attention in assessing genetic conditions.

Artificial intelligence (AI) for facial diagnostics is increasingly used in the genetics clinic to e...

Assisted Robots in Therapies for Children with Autism in Early Childhood.

Children with autism spectrum disorder (ASD) have deficits that affect their social relationships, c...

Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges.

Aerobic composting stands as a widely-adopted method for treating organic solid waste (OSW), simulta...

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

PURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are cond...