AIMC Topic: Genetic Predisposition to Disease

Clear Filters Showing 101 to 110 of 315 articles

Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants.

Biology open
Distinguishing clinical subgroups for patients suffering with diseases characterized by a wide phenotypic spectrum is essential for developing precision therapies. Patients with gain-of-function (GOF) variants in the SCN8A gene exhibit substantial cl...

Graph Node Classification to Predict Autism Risk in Genes.

Genes
This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome...

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Human genetics
Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an...

Predicting miRNA-Disease Associations by Combining Graph and Hypergraph Convolutional Network.

Interdisciplinary sciences, computational life sciences
miRNAs are important regulators for many crucial biological processes. Many recent studies have shown that miRNAs are closely related to various human diseases and can be potential biomarkers or therapeutic targets for some diseases, such as cancers....

Hessian Regularized -Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Interdisciplinary sciences, computational life sciences
Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate...

Machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism.

Journal of biomolecular structure & dynamics
The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 6...

DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.

PLoS computational biology
The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene driv...

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.

PLoS computational biology
Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygen...

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.

Circulation. Genomic and precision medicine
BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well u...

Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models.

International journal of molecular sciences
Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case-control st...