Autism

The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm.

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to ...

Clustering of soybean genotypes via Ward-MLM and ANNs associated with mixed models.

The objectives of this study were to use mixed models to confirm the presence of genetic variability in 16 soybean genotypes, to compare clusters generated by artificial neural networks (ANNs) with those created by the Ward modified location model (MLM) technique, and to indicate parental combinations that hold promise for obtaining superior segregating populations of soybean. A field trial was co...

Atypical Asymmetry for Processing Human and Robot Faces in Autism Revealed by fNIRS.

Deficits in the visual processing of faces in autism spectrum disorder (ASD) individuals may be due to atypical brain organization and function. Studi...

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through ...

Supporting Regularized Logistic Regression Privately and Efficiently.

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, so...

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, p...

Tracking medical genetic literature through machine learning.

There has been remarkable progress in identifying the causes of genetic conditions as well as understanding how changes in specific genes cause diseas...

Prediction of brain maturity in infants using machine-learning algorithms.

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, se...

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion.

BACKGROUND: Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have note...

Ecological systems as computer networks: Long distance sea dispersal as a communication medium between island plant populations.

Ecological systems are known to exchange genetic material through animal species migration and seed dispersal for plants. Isolated plant populations h...

Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.

The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The presen...

Deep Learning for Population Genetic Inference.

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumve...

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system ...

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov.

OBJECTIVE: Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer ther...

Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior.

The unified theory of reinforcement has been used to develop models of behavior over the last 20 years (Donahoe et al., 1993). Previous research has f...

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modal...

Genes with high penetrance for syndromic and non-syndromic autism typically function within the nucleus and regulate gene expression.

BACKGROUND: Intellectual disability (ID), autism, and epilepsy share frequent yet variable comorbidities with one another. In order to better understa...

Vitamin D Deficiency in Obsessive-Compulsive Disorder Patients with Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections: A Case Control Study.

INTRODUCTION: Previous studies have indicated that vitamin D deficiency is common in psychiatric patients, particularly in those with neuropsychiatric...

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseas...

Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is...