Autism

Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study.

BACKGROUND: Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduce...

Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.

The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically develop...

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcript...

Application of Single-Nucleotide Polymorphisms in the Diagnosis of Autism Spectrum Disorders: A Preliminary Study with Artificial Neural Networks.

Autism spectrum disorder (ASD) includes different neurodevelopmental disorders characterized by deficits in social communication, and restricted, repe...

Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation.

Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents under...

Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming.

The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision task...

A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses.

There are currently no standardized objective measures to evaluate beef flavor attributes, especially the comparison between raw beef and cooked beef....

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding.

An enhancer is a short (50-1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic varia...

Job interview training targeting nonverbal communication using an android robot for individuals with autism spectrum disorder.

Job interviews are significant barriers for individuals with autism spectrum disorder because these individuals lack good nonverbal communication skil...

Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies.

Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods...

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have d...

Pilot Testing a Robot for Reducing Pain in Hospitalized Preterm Infants.

Optimizing neurodevelopment is a key goal of neonatal occupational therapy. In preterm infants, repeated procedural pain is associated with adverse ef...

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics with bigger data and wider applications.

The sixth International Conference on Intelligent Biology and Medicine (ICIBM) took place in Los Angeles, California, USA on June 10-12, 2018. This co...

Neurodevelopmental heterogeneity and computational approaches for understanding autism.

In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the p...

Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network.

The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a n...

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction.

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we ap...

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters.

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement pro...

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in ...

Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition.

BACKGROUND: Individuals with autism spectrum disorder (ASD) tend to show deficits in engaging with humans. Previous findings have shown that robot-bas...

A Recursive Approach to Quantized H State Estimation for Genetic Regulatory Networks Under Stochastic Communication Protocols.

This paper deals with the finite-horizon quantized H state estimation problem for a class of discrete time-varying genetic regulatory networks with qu...