AIMC Topic: Multifactorial Inheritance

Clear Filters Showing 31 to 40 of 56 articles

Deep neural network improves the estimation of polygenic risk scores for breast cancer.

Journal of human genetics
Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A de...

Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.

Nature communications
The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on compl...

Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.

Genetics, selection, evolution : GSE
BACKGROUND: Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine...

Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status.

Genetic epidemiology
Coronary artery disease (CAD) is the leading global cause of mortality and has substantial heritability with a polygenic architecture. Recent approaches of risk prediction were based on polygenic risk scores (PRS) not taking possible nonlinear effect...

Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases.

Artificial intelligence in medicine
In order to gain insight into oligogenic disorders, understanding those involving bi-locus variant combinations appears to be key. In prior work, we showed that features at multiple biological scales can already be used to discriminate among two type...

A Guide for Using Deep Learning for Complex Trait Genomic Prediction.

Genes
Deep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoreti...

Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

Genetics
The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNN...

Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism.

Alcoholism, clinical and experimental research
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
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...

Diagnosing migraine from genome-wide genotype data: a machine learning analysis.

Brain : a journal of neurology
Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the heritability. We aimed to develop machine learning models, capturing non-additive and interactive ef...