<p>For genotype data, we associate each gene with a single SNP (a). Next, we calculate correlation statistics using the gene-based data for each data type (b). We then calculate enrichment scores using the correlation statistics for each data type (c). Finally, we independently build a predictive model for each data type using the enrichment scores for each data type and a standard SVM (d). In this overview, ASSESS corresponds to steps b and c.</p
Abstract Background Quantitative phenotypes emerge everywhere in systems biology and biomedicine due...
(a) Given the GWAS of a complex trait, we define trait-associated regions by first identifying varia...
<p>SNP markers from GWAS data were assigned to single genes in a process termed “gene binning”, by i...
<div><p>Understanding the root molecular and genetic causes driving complex traits is a fundamental ...
The availability of the genetic background of large sample groups motivates genome-wide association ...
Information about small genetic variations in organisms, known as single nucleotide polymorphism (SN...
Motivation: Biomarker discovery and gene ranking is a standard task in genomic high throughput analy...
Genome wide association studies (GWAS) search for correlations between single nucleotide polymorphis...
<p>A. Initially, SNP association p-values are produced by an association test. Based on these values...
Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influenci...
<p>When exploring SNP-gene expression associations, GenAMap provides links to tools that allow the a...
Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards und...
Starting with liver, limb and brain enhancers and genomic background regions from six mammals, the f...
BACKGROUND: Genome-wide association studies (GWAS) aim to identify causal variants and genes for com...
<p>Colored boxes (gray/green) depict different training data sets. Step 1- assessment of individual ...
Abstract Background Quantitative phenotypes emerge everywhere in systems biology and biomedicine due...
(a) Given the GWAS of a complex trait, we define trait-associated regions by first identifying varia...
<p>SNP markers from GWAS data were assigned to single genes in a process termed “gene binning”, by i...
<div><p>Understanding the root molecular and genetic causes driving complex traits is a fundamental ...
The availability of the genetic background of large sample groups motivates genome-wide association ...
Information about small genetic variations in organisms, known as single nucleotide polymorphism (SN...
Motivation: Biomarker discovery and gene ranking is a standard task in genomic high throughput analy...
Genome wide association studies (GWAS) search for correlations between single nucleotide polymorphis...
<p>A. Initially, SNP association p-values are produced by an association test. Based on these values...
Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influenci...
<p>When exploring SNP-gene expression associations, GenAMap provides links to tools that allow the a...
Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards und...
Starting with liver, limb and brain enhancers and genomic background regions from six mammals, the f...
BACKGROUND: Genome-wide association studies (GWAS) aim to identify causal variants and genes for com...
<p>Colored boxes (gray/green) depict different training data sets. Step 1- assessment of individual ...
Abstract Background Quantitative phenotypes emerge everywhere in systems biology and biomedicine due...
(a) Given the GWAS of a complex trait, we define trait-associated regions by first identifying varia...
<p>SNP markers from GWAS data were assigned to single genes in a process termed “gene binning”, by i...