Autism

Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) was used to investigate spontaneous hemody...

DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction.

Detecting epistatic interaction is a typical way of identifying the genetic susceptibility of comple...

ImaGene: a convolutional neural network to quantify natural selection from genomic data.

BACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to th...

The Hearing Impairment Ontology: A Tool for Unifying Hearing Impairment Knowledge to Enhance Collaborative Research.

Hearing impairment (HI) is a common sensory disorder that is defined as the partial or complete inab...

Artificial intelligence in genetic services delivery: Utopia or apocalypse?

Artificial intelligence (AI) technologies have a long history, with increasing presence and potentia...

Genetic Variation in Cytochrome P450 2R1 and Vitamin D Binding Protein Genes are associated with Vitamin D Deficiency in Adolescents.

Genome Wide Association Studies (GWAS) have evaluated several genes related to vitamin D synthesis,...

A genetic programming-based approach to identify potential inhibitors of serine protease of .

We applied genetic programming approaches to understand the impact of descriptors on inhibitory eff...

WormBot, an open-source robotics platform for survival and behavior analysis in C. elegans.

Caenorhabditis elegans is a popular organism for aging research owing to its highly conserved molecu...

A review on neural network models of schizophrenia and autism spectrum disorder.

This survey presents the most relevant neural network models of autism spectrum disorder and schizop...

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach.

Machine learning methods show promise to translate univariate biomarker findings into clinically use...

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Treating patients with major depressive disorder is challenging because it takes several months for ...

Jointly Integrating VCF-Based Variants and OWL-Based Biomedical Ontologies in MongoDB.

The development of the next-generation sequencing (NGS) technologies has led to massive amounts of V...

Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers.

OBJECTIVE: Accurate prediction of treatment responses in rheumatoid arthritis (RA) patients can prov...

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.

There have been several empirical studies addressing breast cancer using machine learning and soft c...

Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.

As a large amount of genetic data are accumulated, an effective analytical method and a significant ...

Robot-based play-drama intervention may improve the narrative abilities of Chinese-speaking preschoolers with autism spectrum disorder.

BACKGROUND: Children with autism spectrum disorder (ASD) have deficits in their narrative skills and...

Challenges to increase the AI and ET markets in Brazil.

Artificial insemination (AI) and embryo transfer (ET) are the most widely used biotechnologies in th...

Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale.

The identification of autistic individuals using resting state functional connectivity networks can ...

GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine.

Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment ...

A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1.

A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.

OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive proces...