Autism

Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

BACKGROUND: Previous automated EEG-based diagnosis of autism spectrum disorders (ASD) using various nonlinear EEG analysis methods were limited to distinguish only children with ASD from those normally developed without approaching their autistic features severity.

Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification.

Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) has challenged researchers for many years. With enough data and computational power, machine le...

Identifying facial phenotypes of genetic disorders using deep learning.

Syndromic genetic conditions, in aggregate, affect 8% of the population. Many syndromes have recognizable facial features that are highly informative ...

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial...

Parameters of stochastic models for electroencephalogram data as biomarkers for child's neurodevelopment after cerebral malaria.

The objective of this study was to test statistical features from the electroencephalogram (EEG) recordings as predictors of neurodevelopment and cogn...

Project INSIDE: towards autonomous semi-unstructured human-robot social interaction in autism therapy.

This paper describes the INSIDE system, a networked robot system designed to allow the use of mobile robots as active players in the therapy of childr...

Group Differences in Facial Emotion Expression in Autism: Evidence for the Utility of Machine Classification.

Effective social communication relies, in part, on accurate nonverbal expression of emotion. To evaluate the nature of facial emotion expression (FEE)...

Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft.

The isolated type of orofacial cleft, termed non-syndromic cleft lip with or without cleft palate (NSCL/P), is the second most common birth defect in ...

Computer vision and behavioral phenotyping: an autism case study.

Despite significant recent advances in molecular genetics and neuroscience, behavioral ratings based on clinical observations are still the gold stand...

A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms.

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autis...

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases pres...

Optimizing Corpus Creation for Training Word Embedding in Low Resource Domains: A Case Study in Autism Spectrum Disorder (ASD).

Automating the extraction of behavioral criteria indicative of Autism Spectrum Disorder (ASD) in electronic health records (EHRs) can contribute signi...

Toward a motor signature in autism: Studies from human-machine interaction.

BACKGROUND: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders which core symptoms are impairments in socio-commu...

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and progn...

Mobile detection of autism through machine learning on home video: A development and prospective validation study.

BACKGROUND: The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to compl...

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors.

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with majo...

Pectin extraction from Helianthus annuus (sunflower) heads using RSM and ANN modelling by a genetic algorithm approach.

In this work, Response Surface Methodology (RSM) and Artificial Neural Network coupled with genetic algorithm (ANN-GA) have been used to develop a mod...

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application.

BACKGROUND: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the C...

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions i...

Prenatal Exposure to Gestational Nicotine before Neurulation is Detrimental to Neurodevelopment of Wistar Rats' Offspring.

BACKGROUND AND AIM: This study aimed to determine the effect of gestational nicotine exposure before neurodevelopment on the morphology and histology ...