AIMC Topic: Multifactorial Inheritance
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Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism.
Alcoholism, clinical and experimental research
Jun 13, 2018
BACKGROUND: A statistical pipeline was developed and used for determining candidate genes and candidate gene coexpression networks involved in 2 alcohol (i.e., ethanol [EtOH]) metabolism phenotypes, namely alcohol clearance and acetate area under the...
UGMDR: a unified conceptual framework for detection of multifactor interactions underlying complex traits.
Heredity
Oct 22, 2014
Biological outcomes are governed by multiple genetic and environmental factors that act in concert. Determining multifactor interactions is the primary topic of interest in recent genetics studies but presents enormous statistical and mathematical ch...
MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.
Briefings in bioinformatics
Mar 4, 2025
Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that bridge the genetics and clinical manifestations of human disease. However, the genetic architecture of t...
PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network.
Bioinformatics (Oxford, England)
Feb 4, 2025
SUMMARY: Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power remains limited for many diseases in the general population. As a result, numerous novel PRS modeling te...
[From AI to polygenic risk scores: which innovations will shape the future of psychiatry?].
Tijdschrift voor psychiatrie
Jan 1, 2025
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).
Deep learning insights into distinct patterns of polygenic adaptation across human populations.
Nucleic acids research
Dec 11, 2024
Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter t...
Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.
European heart journal
Dec 7, 2024
BACKGROUND AND AIMS: Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing i...
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
Aug 3, 2024
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...
Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.
Journal of Alzheimer's disease : JAD
Jan 1, 2024
BACKGROUND: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail t...
Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks.
Molecular biology and evolution
Oct 4, 2023
Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which use...
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