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Thomas H. Hampton

dblp:209/7918 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
data mining
0.312017
ScanGEO: parallel mining of high-throughput gene expression data · Bioinform. 2017
Bioinformatics and computational biology › gene expression analysis
differential expression analysis
0.312017
ScanGEO: parallel mining of high-throughput gene expression data · Bioinform. 2017
Bioinformatics and computational biology
gene expression
0.312017
ScanGEO: parallel mining of high-throughput gene expression data · Bioinform. 2017

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

shiny · 0.3r · 0.3
YearPublicationVenuePosition
2017 ScanGEO: parallel mining of high-throughput gene expression data
abstract
SUMMARY: Current options to mine publicly available gene expression data deposited in NCBI's gene expression omnibus (GEO), such as the GEO web portal and related applications, are optimized to reanalyze a single study, or search for a single gene, and therefore require manual intervention to reanalyze multiple studies for user-specified gene sets. ScanGEO is a simple, user-friendly Shiny web application designed to identify differentially expressed genes across all GEO studies matching user-specified criteria, for a flexible set of genes, visualize results and provide summary statistics and other reports using a single command. AVAILABILITY AND IMPLEMENTATION: The ScanGEO source code is written in R and implemented as a Shiny app that can be freely accessed at http://scangeo.dartmouth.edu/ScanGEO/. For users who would like to run a local instantiation of the app, the R source code is available under a GNU GPLv3 license at https://github.com/StantonLabDartmouth/AppScanGEO. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Katja Koeppen, Bruce A. Stanton, Thomas H. Hampton
Bioinform.3