Neurology

Autism

Latest AI and machine learning research in autism for healthcare professionals.

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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified ...

PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution.

BACKGROUND: Our understanding of polyploid genomes is limited by our inability to definitively assig...

Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes.

To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism,...

In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm.

Hemolytic toxicity of small molecules, as one of the important ADMET end points, can cause the lysis...

Applying Deep Neural Network Analysis to High-Content Image-Based Assays.

The etiological underpinnings of many CNS disorders are not well understood. This is likely due to t...

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

A central challenge in human genomics is to understand the cellular, evolutionary, and clinical sign...

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm.

Imaging studies have characterized functional and structural brain abnormalities in adults after pre...

Role of deep learning in infant brain MRI analysis.

Deep learning algorithms and in particular convolutional networks have shown tremendous success in m...

fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.

BACKGROUND: Parametric feature selection methods for machine learning and association studies based ...

Identifying genetic determinants of complex phenotypes from whole genome sequence data.

BACKGROUND: A critical goal in biology is to relate the phenotype to the genotype, that is, to find ...

Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences.

Statistical models of the human body surface are generally learned from thousands of high-quality 3D...

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here,...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is ce...

Wilson's disease: A new perspective review on its genetics, diagnosis and treatment.

Wilson's disease (WD) is an autosomal recessive disorder which is caused by poor excretion of copper...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functionin...

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep...

Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning.

Preclinical studies of psychiatric disorders use animal models to investigate the impact of environm...

Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability.

In the terrestrial ecosystems, perennial challenges of increased frequency and intensity of wildfire...

nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.

Quantifying causal (effective) interactions between different brain regions are very important in ne...

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