AIMC Topic: Genome-Wide Association Study

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Using recursive feature elimination in random forest to account for correlated variables in high dimensional data.

BMC genetics
BACKGROUND: Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact it...

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
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 ...

MCRiceRepGP: a framework for the identification of genes associated with sexual reproduction in rice.

The Plant journal : for cell and molecular biology
Rice is an important cereal crop, being a staple food for over half of the world's population, and sexual reproduction resulting in grain formation underpins global food security. However, despite considerable research efforts, many of the genes, esp...

A deep convolutional neural network approach for predicting phenotypes from genotypes.

Planta
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted b...

An unsupervised machine learning method for discovering patient clusters based on genetic signatures.

Journal of biomedical informatics
INTRODUCTION: Many chronic disorders have genomic etiology, disease progression, clinical presentation, and response to treatment that vary on a patient-to-patient basis. Such variability creates a need to identify characteristics within patient popu...

Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.

Nature genetics
Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because ...

PLANET-SNP pipeline: PLants based ANnotation and Establishment of True SNP pipeline.

Genomics
Acute prediction of SNPs (Single Nucleotide Polymorphisms) from high throughput sequencing data is a challenging problem, having potential to explore possible variation within plants species. For the extraction of profitable information from bulk of ...

Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning.

Methods (San Diego, Calif.)
Genome-wide association studies (GWAS) have successfully discovered a number of disease-associated genetic variants in the past decade, providing an unprecedented opportunity for deciphering genetic basis of human inherited diseases. However, it is s...

Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity.

Genomics
The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computat...