AIMC Topic: Epistasis, Genetic
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Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes.
BioMed research international
Jul 11, 2019
To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multi...
Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis.
IEEE/ACM transactions on computational biology and bioinformatics
Nov 5, 2018
Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping...
Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.
Genetic epidemiology
Nov 2, 2018
The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as dise...
Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification.
BMC bioinformatics
Sep 18, 2018
BACKGROUND: Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multif...
Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.
IEEE/ACM transactions on computational biology and bioinformatics
Sep 3, 2018
Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype (i.e., disease ...
Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.
PloS one
Jul 26, 2018
The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene ...
Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions.
IEEE/ACM transactions on computational biology and bioinformatics
Jul 23, 2018
Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach f...
Machine learning algorithm-based risk prediction model of coronary artery disease.
Molecular biology reports
Jul 11, 2018
In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic ri...
Female
Risk Factors
Aged
Adult
Male
Middle Aged
Multifactor Dimensionality Reduction
Machine Learning
Xenobiotics
Humans
Coronary Artery Disease
Case-Control Studies
Algorithms
Cytochrome P-450 CYP1A1
Genetic Predisposition to Disease
Folic Acid
Forecasting
Glycine Hydroxymethyltransferase
Polymorphism, Single Nucleotide
Epistasis, Genetic
Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions.
BMC medical genomics
Apr 20, 2018
BACKGROUND: Gene-gene interactions (GGIs) are a known cause of missing heritability. Multifactor dimensionality reduction (MDR) is one of most commonly used methods for GGI detection. The generalized multifactor dimensionality reduction (GMDR) method...
Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.
Scientific reports
Apr 18, 2018
Understanding genetic mechanism of complex diseases is a serious challenge. Existing methods often neglect the heterogeneity phenomenon of complex diseases, resulting in lack of power or low reproducibility. Addressing heterogeneity when detecting ep...
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