Autism

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responder...

Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.

Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning mo...

Functional Categorization of Disease Genes Based on Spectral Graph Theory and Integrated Biological Knowledge.

Interaction of multiple genetic variants is a major challenge in the development of effective treatment strategies for complex disorders. Identifying ...

Risk-Predicting Model for Incident of Essential Hypertension Based on Environmental and Genetic Factors with Support Vector Machine.

Essential hypertension (EH) has become a major chronic disease around the world. To build a risk-predicting model for EH can help to interpose people'...

Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning.

OBJECTIVE: Use of opioids for pain management has increased over the past decade; however, inadequate analgesic response is common. Genetic variabilit...

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.

BACKGROUND: Alzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomed...

An end-to-end deep learning architecture for extracting protein-protein interactions affected by genetic mutations.

The BioCreative VI Track IV (mining protein interactions and mutations for precision medicine) challenge was organized in 2017 with the goal of applyi...

AutismKB 2.0: a knowledgebase for the genetic evidence of autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with strong genetic contributions. To provide a comprehensive resource for the...

Variants in GNPTAB, GNPTG and NAGPA genes are associated with stutterers.

Non-syndromic stuttering is a neurodevelopmental disorder characterized by disruptions in normal flow of speech in the form of repetition, prolongatio...

Real-Time Monitoring and Analysis of Zebrafish Electrocardiogram with Anomaly Detection.

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac d...

Pseudoneglect in Visual Search: Behavioral Evidence and Connectional Constraints in Simulated Neural Circuitry.

Most people tend to bisect horizontal lines slightly to the left of their true center (pseudoneglect) and start visual search from left-sided items. T...

Metric learning with spectral graph convolutions on brain connectivity networks.

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recogniti...

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction...

Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism.

BACKGROUND: Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the...

Modeling the control of the central nervous system over the cardiovascular system using support vector machines.

The control of the central nervous system (CNS) over the cardiovascular system (CS) has been modeled using different techniques, such as fuzzy inducti...

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia.

Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy r...

Augmented reality for personalized nanomedicines.

As our understanding of onset and progress of diseases at the genetic and molecular level rapidly progresses, the potential of advanced technologies, ...

The Metabolic Response of Skeletal Muscle to Endurance Exercise Is Modified by the ACE-I/D Gene Polymorphism and Training State.

The insertion/deletion polymorphism in the gene for the regulator of vascular tone, angiotensin-converting enzyme (ACE), is the prototype of a genetic...

A novel method for predicting kidney stone type using ensemble learning.

The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the...

Machine learning shows association between genetic variability in and cerebral connectivity in preterm infants.

Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular an...