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Autism Spectrum Disorder

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Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification.

Brain research
Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In thi...

MVP predicts the pathogenicity of missense variants by deep learning.

Nature communications
Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. H...

Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To classify children with autism spectrum disorder (ASD) and typical development (TD) using short-term spontaneous hemodynamic fluctuations and to explore the abnormality of inferior frontal gyrus and temporal lobe in ASD.

Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder.

BMC medical informatics and decision making
BACKGROUND: Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most w...

Detection of eye contact with deep neural networks is as accurate as human experts.

Nature communications
Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject's looki...

Feature replacement methods enable reliable home video analysis for machine learning detection of autism.

Scientific reports
Autism Spectrum Disorder is a neuropsychiatric condition affecting 53 million children worldwide and for which early diagnosis is critical to the outcome of behavior therapies. Machine learning applied to features manually extracted from readily acce...

Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation.

Sensors (Basel, Switzerland)
Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) chil...

Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony.

Neuroscience letters
Autism spectrum disorder (ASD) is a brain disorder that develops during an early stage of childhood. Previous neuroimaging-based diagnostic models for ASD were based on static functional connectivity (FC). The nonlinear complexity of brain connectivi...

Robot dramas may improve joint attention of Chinese-speaking low-functioning children with autism: stepped wedge trials.

Disability and rehabilitation. Assistive technology
INTRODUCTION: Children with autism spectrum disorder (ASD), especially those with low cognitive functioning, have deficits in joint attention. Previous research has found that these children are interested in engaging with social robots.

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.

BMC bioinformatics
BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the cur...