Autism

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation...

Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns.

Autism spectrum disorder (ASD) is an age- and sex-related neurodevelopmental disorder that alters the brain's functional connectivity (FC). The changes caused by ASD are associated with different age- and sex-related patterns in neuroimaging data. However, most contemporary computer-assisted ASD diagnosis methods ignore the aforementioned age-/sex-related patterns. In this paper, we propose a nove...

Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning.

Genome-wide association studies (GWAS) have successfully discovered a number of disease-associated genetic variants in the past decade, providing an u...

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.

Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical appli...

Robot-based intervention may reduce delay in the production of intransitive gestures in Chinese-speaking preschoolers with autism spectrum disorder.

BACKGROUND: Past studies have shown that robot-based intervention was effective in improving gestural use in children with autism spectrum disorders (...

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patt...

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While ti...

Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.

Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrosp...

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach.

BACKGROUND: Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated ...

A machine learning based framework to identify and classify long terminal repeat retrotransposons.

Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within...

A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.

Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases....

GGDonto ontology as a knowledge-base for genetic diseases and disorders of glycan metabolism and their causative genes.

BACKGROUND: Inherited mutations in glyco-related genes can affect the biosynthesis and degradation of glycans and result in severe genetic diseases an...

Detection of Suicidality in Adolescents with Autism Spectrum Disorders: Developing a Natural Language Processing Approach for Use in Electronic Health Records.

Over 15% of young people with autism spectrum disorders (ASD) will contemplate or attempt suicide during adolescence. Yet, there is limited evidence c...

Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.

Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance...

Brain-specific functional relationship networks inform autism spectrum disorder gene prediction.

Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have result...

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

BACKGROUND: Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and gene...

High efficiency classification of children with autism spectrum disorder.

Autism spectrum disorder (ASD) is a wide-ranging collection of developmental diseases with varying symptoms and degrees of disability. Currently, ASD ...

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilitie...

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate...

Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.

This article provides data for five different neuropsychiatric disorders-Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectru...