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Robnet T. Kerns

dblp:46/5998 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
differential expression analysis
0.112006
SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006
Bioinformatics and computational biology
gene expression analysis
0.112006
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
0.112006
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
0.112006
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
YearPublicationVenuePosition
2006 SScore: an R package for detecting differential gene expression without gene expression summaries
abstract
SUMMARY: 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