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Anders Gabrielsen

dblp:18/7251 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · unresolved

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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › quantitative trait locus mapping
expression quantitative trait loci analysis
0.112009
GeneRegionScan: a Bioconductor package for probe-level analysis of specific, small regions of the genome · Bioinform. 2009
Bioinformatics and computational biology
transcriptomics
0.112009
GeneRegionScan: a Bioconductor package for probe-level analysis of specific, small regions of the genome · Bioinform. 2009

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

linear regression · 0.1
YearPublicationVenuePosition
2009 GeneRegionScan: a Bioconductor package for probe-level analysis of specific, small regions of the genome
abstract
SUMMARY: Whole-genome microarrays allow us to interrogate the entire transcriptome of a cell. Affymetrix microarrays are constructed using several probes that match to different regions of a gene and a summarization step reduces this complexity into a single value, representing the expression level of the gene or the expression level of an exon in the case of exon arrays. However, this simplification eliminates information that might be useful when focusing on specific genes of interest. To address these limitations, we present a software package for the R platform that allows detailed analysis of expression at the probe level. The package matches the probe sequences against a target gene sequence (either mRNA or DNA) and shows the expression levels of each probe along the gene. It also features functions to fit a linear regression based on several genetic models that enables study of the relationship between gene expression and genotype. AVAILABILITY AND IMPLEMENTATION: The software is implemented as a platform-independent R package available through the Bioconductor repository at http://www.bioconductor.org/. It is licensed as GPL 2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Lasse Folkersen, Diego Diez, Craig E. Wheelock, Jesper Z. Haeggström, Susumu Goto, Per Eriksson, Anders Gabrielsen
Bioinform.7