AIMC Topic: Genome-Wide Association Study
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Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.
Scientific reports
May 30, 2024
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baselin...
Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.
Genes
May 26, 2024
Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increa...
Stacked neural network for predicting polygenic risk score.
Scientific reports
May 21, 2024
In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential ...
Deep learning of left atrial structure and function provides link to atrial fibrillation risk.
Nature communications
May 21, 2024
Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to asse...
A deep learning approach to explore the association of age-related macular degeneration polygenic risk score with retinal optical coherence tomography: A preliminary study.
Acta ophthalmologica
May 18, 2024
PURPOSE: Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans.
Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.
Science advances
May 8, 2024
The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca'...
Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning.
The Australian and New Zealand journal of psychiatry
Apr 6, 2024
OBJECTIVE: Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic di...
Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network.
Computers in biology and medicine
Mar 25, 2024
Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, with numerous approaches having identified genes linked to its development, diagnosis and prognosis. Despite genome-wide association analysis and gene...
Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.
Journal of human genetics
Jan 15, 2024
The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation me...
Machine Learning to Advance Human Genome-Wide Association Studies.
Genes
Dec 25, 2023
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from large...
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