Autism

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints.

Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes po...

Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for...

Machine learning for genomic and pedigree prediction in sugarcane.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its hete...

Shaping dynamical neural computations using spatiotemporal constraints.

Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represen...

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interact...

A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot.

To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relatio...

The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research.

Viral-like particles (VLPs) represent versatile nanoscale structures mimicking the morphology and antigenic characteristics of viruses, devoid of gene...

Attention Analysis in Robotic-Assistive Therapy for Children With Autism.

Children with Autism Spectrum Disorder (ASD) show severe attention deficits, hindering their capacity to acquire new skills. The automatic assessment ...

Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.

Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learni...

Cefixime removal via WO/Co-ZIF nanocomposite using machine learning methods.

In this research, an upgraded and environmentally friendly process involving WO/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqu...

Using AI-Based Virtual Companions to Assist Adolescents with Autism in Recognizing and Addressing Cyberbullying.

Social media platforms and online gaming sites play a pervasive role in facilitating peer interaction and social development for adolescents, but they...

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.

The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatmen...

Autism spectrum disorders detection based on multi-task transformer neural network.

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying A...

Artificial intelligence enables unified analysis of historical and landscape influences on genetic diversity.

While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with...

A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods.

The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting ...

Discovering the gene-brain-behavior link in autism via generative machine learning.

Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment o...

Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.

Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico scor...

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.

Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the ...

A Kernelized Classification Approach for Cancer Recognition Using Markovian Analysis of DNA Structure Patterns as Feature Mining.

Nucleotide-based molecules called DNA and RNA are essential for several biological processes that affect both normal and cancerous cells. They contain...

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry.

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinic...