Demo Data from SIGIR 2026 / reviewers in the wild / expert
Robnet T. Kerns
dblp:46/5998
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—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 › gene expression analysis
differential expression analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Bioinformatics and computational biology
gene expression analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Bioinformatics and computational biology › gene expression analysis
microarray data analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Bioinformatics and computational biology › gene expression analysis › microarray data preprocessing
probe-level analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Methods — techniques the papers use, named apart from their topics
s-score algorithm · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | SScore: an R package for detecting differential gene expression without gene expression summariesabstractSUMMARY: SScore is an R package that facilitates the comparison of gene expression between Affymetrix GeneChips using the S-score algorithm. The S-score algorithm uses probe level data directly to assess differences in gene expression, without requiring a preliminary separate step of probe set expression summary estimation. Therefore, the algorithm avoids introduction of error associated with the expression summary estimation process and has been demonstrated to improve the accuracy of identifying differentially expressed genes. The S-score produces accurate results even when few or no replicates are available. AVAILABILITY: The R package SScore is available from Bioconductor at http://www.bioconductor.org Richard E. Kennedy, Robnet T. Kerns, Xiangrong Kong, Kellie J. Archer, Michael F. Miles |
Bioinform. | 2 |