Autism

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 the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overcome these challenges, researchers are integrating novel data types from numerous patients, including imaging studies capturing broadly applicable featu...

Using Recurrent Neural Networks to Compare Movement Patterns in ADHD and Normally Developing Children Based on Acceleration Signals from the Wrist and Ankle.

Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects, among other things, the movement patterns of children suffering it. Inattention, hyperactivity and impulsive behaviors, major symptoms characterizing ADHD, result not only in differences in the activity levels but also in the activity patterns themselves. This paper proposes and trains a Recurrent N...

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 significance of genetic variants. Here, we introduce a...

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 premature birth, but these investigations have mostly...

Role of deep learning in infant brain MRI analysis.

Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relativ...

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 on genetic data are not robust with respect to out...

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 scans in predefined poses to cover the wide varie...

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequ...

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

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). ...

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 in mammalian cells. In this review, various issue...

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 functioning of the mind or brain. In the last few years, the...

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-learning-based framework that predicts the specif...

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 environmental factors or genetic mutations on complex trai...

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 neuroscience research. Many conventional methods est...

Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning.

BACKGROUND AND OBJECTIVE: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary e...

Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy.

BACKGROUND: Research in embodied artificial intelligence (AI) has increasing clinical relevance for therapeutic applications in mental health services...

Statistical learning approaches in the genetic epidemiology of complex diseases.

In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional gene...

Comedic experience with two robots aided a child with autism spectrum disorder to realize the importance of nonverbal communication.

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child's wellbeing, especially in the...

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy.

Autism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to in...