AIMC Topic: Epistasis, Genetic
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Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.
Gene
Jan 16, 2016
In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how m...
A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype.
BioMed research international
Aug 3, 2015
Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic vari...
Multivariate Quantitative Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions.
Human heredity
Jul 28, 2015
OBJECTIVES: To determine gene-gene interactions and missing heritability of complex diseases is a challenging topic in genome-wide association studies. The multifactor dimensionality reduction (MDR) method is one of the most commonly used methods for...
An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction.
BMC genomics
Jul 1, 2015
BACKGROUND: Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations mak...
Multifactor dimensionality reduction analysis to elucidate the cross-talk between one-carbon and xenobiotic metabolic pathways in multi-disease models.
Molecular biology reports
Feb 4, 2015
Putatively functional polymorphisms of one-carbon and xenobiotic metabolic pathways influence susceptibility for wide spectrum of diseases. The current study was aimed to explore gene-gene interactions among these two metabolic pathways in four disea...
Gene Expression Regulation
Epistasis, Genetic
Oxidoreductases
Xenobiotics
Transferases
Multifactor Dimensionality Reduction
Lupus Erythematosus, Systemic
Humans
Coronary Artery Disease
Sex Factors
Datasets as Topic
Case-Control Studies
Parkinson Disease
Female
Genetic Predisposition to Disease
Polymorphism, Single Nucleotide
Metabolic Networks and Pathways
Breast Neoplasms
Carbon
Factor Analysis, Statistical
Male
The limitations of simple gene set enrichment analysis assuming gene independence.
Statistical methods in medical research
Oct 14, 2012
Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess en...
KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.
Briefings in bioinformatics
Nov 22, 2024
Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this...
Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.
Briefings in bioinformatics
Jan 22, 2024
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, lead...
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...
Accurately modeling biased random walks on weighted networks using node2vec.
Bioinformatics (Oxford, England)
Jan 1, 2023
MOTIVATION: Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased...
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