Autism

Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks.

Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cro...

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture ...

Brain MRI analysis using a deep learning based evolutionary approach.

Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear unde...

Discrimination of and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization.

, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical trea...

Multi-modular AI Approach to Streamline Autism Diagnosis in Young Children.

Autism has become a pressing healthcare challenge. The instruments used to aid diagnosis are time and labor expensive and require trained clinicians t...

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning.

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the...

Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD.

BACKGROUND: Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and ina...

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjecti...

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers.

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic-ischemic encephalop...

Optimization of culture conditions for differentiation of melon based on artificial neural network and genetic algorithm.

Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for predi...

A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers.

Run-off-road (ROR) crashes have always been a major concern as this type of crash is usually associated with a considerable number of serious injury a...

PANDA: Prioritization of autism-genes using network-based deep-learning approach.

Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of ...

Tip-Over Stability Analysis of a Pelvic Support Walking Robot.

Discussed in this paper is the tip-over stability analysis of a pelvic support walking robot. To improve the activities of daily living (ADL) in hemip...

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning.

AIMS: Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testin...

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly diff...

Artificial Intelligence for Cardiac Imaging-Genetics Research.

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. ...

Improving the detection of autism spectrum disorder by combining structural and functional MRI information.

Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as res...

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.

Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain h...

Determining of the optimized dimensions of the Marinelli beaker containing source with inhomogeneous emission rate by using genetic algorithm coupled with MCNP and determining distribution type by neural networks.

In order to determine the activity of C137s in soil resulting from nuclear accidents or fallouts, the best choice is to use HPGe detectors due to thei...

SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems.

In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body str...