AIMC Topic: Epistasis, Genetic

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A knowledge-based approach for predicting gene-disease associations.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat de...

OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data.

Journal of biomedical semantics
As a special class of non-coding RNAs (ncRNAs), microRNAs (miRNAs) perform important roles in numerous biological and pathological processes. The realization of miRNA functions depends largely on how miRNAs regulate specific target genes. It is there...

Sparse Inverse Covariance Estimation with L0 Penalty for Network Construction with Omics Data.

Journal of computational biology : a journal of computational molecular cell biology
Constructing coexpression and association networks with omics data is crucial for studying gene-gene interactions and underlying biological mechanisms. In recent years, learning the structure of a Gaussian graphical model from high-dimensional data u...

Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

Gene
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
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
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
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...

The limitations of simple gene set enrichment analysis assuming gene independence.

Statistical methods in medical research
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
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...