AIMC Topic: Genome-Wide Association Study

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Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Noncoding single nucleotide polymorphisms (SNPs) and their target genes are important components of the heritability of diseases and other polygenic traits. Identifying these SNPs and target genes could potentially reveal new molecular mechanisms and...

Research progress in machine learning methods for gene-gene interaction detection.

Yi chuan = Hereditas
Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to...

Machine Learning-Based Gene Prioritization Identifies Novel Candidate Risk Genes for Inflammatory Bowel Disease.

Inflammatory bowel diseases
BACKGROUND: The inflammatory bowel diseases (IBDs) are chronic inflammatory disorders, associated with genetic, immunologic, and environmental factors. Although hundreds of genes are implicated in IBD etiology, it is likely that additional genes play...

CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies.

Bioinformatics (Oxford, England)
MOTIVATION: Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to det...

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

Methods in molecular biology (Clifton, N.J.)
Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional p...

A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has b...

Discovery of phenotypic networks from genotypic association studies with application to obesity.

International journal of data mining and bioinformatics
Genome-wide Association Studies (GWAS) have resulted in many discovered risk variants for several obesity-related traits. However, before clinical relevance of these discoveries can be achieved, molecular or physiological mechanisms of these risk var...