AIMC Topic: Genetic Predisposition to Disease

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Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural network.

PloS one
Genome-wide association studies (GWAS) have successfully uncovered numerous associations between genetic variants and disease traits to date. Yet, identifying significantly associated loci remains a considerable challenge due to the concomitant multi...

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

International journal of chronic obstructive pulmonary disease
BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and th...

[From AI to polygenic risk scores: which innovations will shape the future of psychiatry?].

Tijdschrift voor psychiatrie
BACKGROUND: In recent years, developments have been made in various research domains, from treatments with (es)ketamine to large-scale genome-wide association studies (GWAS).

Genome-wide association neural networks identify genes linked to family history of Alzheimer's disease.

Briefings in bioinformatics
Augmenting traditional genome-wide association studies (GWAS) with advanced machine learning algorithms can allow the detection of novel signals in available cohorts. We introduce "genome-wide association neural networks (GWANN)" a novel approach tha...

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Briefings in functional genomics
Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-...

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recen...

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Briefings in bioinformatics
Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for dia...

Discovering predisposing genes for hereditary breast cancer using deep learning.

Briefings in bioinformatics
Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be iden...

SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis.

Briefings in bioinformatics
Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies h...

Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning.

Medicine
BACKGROUND: nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will continue to increase. Genetic susceptibility is an important factor influencing its onset and progressi...