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Bettina Kulle Andreassen

dblp:180/6685 · also Bettina Kulle · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2008
0000-0001-5087-2913ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
gene set enrichment analysis
0.112008
GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies · Bioinform. 2008
Bioinformatics and computational biology › genomics
genome-wide association study
0.112008
GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies · Bioinform. 2008
Bioinformatics and computational biology › genomics › genome-wide association study
pathway-based association analysis
0.112008
GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies · Bioinform. 2008
Bioinformatics and computational biology › statistical genetics › single nucleotide polymorphism analysis
SNP association analysis
0.012008
GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies · Bioinform. 2008

Methods — techniques the papers use, named apart from their topics

statistical testing · 0.1gene set enrichment analysis · 0.1
YearPublicationVenuePosition
2008 GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies
abstract
The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.
Marit Holden, Shiwei Deng, Leszek Wojnowski, Bettina Kulle Andreassen
Bioinform.4