Demo Data from VLDB 2026 / reviewers in the wild / expert
Leszek Wojnowski
dblp:64/7070
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
gene set enrichment analysis |
0.1 | 1 | 2008 | 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 |
0.1 | 1 | 2008 | 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 |
0.1 | 1 | 2008 | 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 |
0.0 | 1 | 2008 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studiesabstractThe 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. | 3 |