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Nicholas W. Wolfe

dblp:172/0393 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › functional genomics
gene function inference
0.212015
ERC analysis: web-based inference of gene function via evolutionary rate covariation · Bioinform. 2015
Bioinformatics and computational biology › network bioinformatics › biological network analysis
gene network analysis
0.212015
ERC analysis: web-based inference of gene function via evolutionary rate covariation · Bioinform. 2015
Bioinformatics and computational biology
comparative genomics
0.112015
ERC analysis: web-based inference of gene function via evolutionary rate covariation · Bioinform. 2015
Bioinformatics and computational biology › evolutionary biology
evolutionary analysis
0.112015
ERC analysis: web-based inference of gene function via evolutionary rate covariation · Bioinform. 2015

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

statistical analysis · 0.2evolutionary rate covariation · 0.2
YearPublicationVenuePosition
2015 ERC analysis: web-based inference of gene function via evolutionary rate covariation
abstract
UNLABELLED: The recent explosion of comparative genomics data presents an unprecedented opportunity to construct gene networks via the evolutionary rate covariation (ERC) signature. ERC is used to identify genes that experienced similar evolutionary histories, and thereby draws functional associations between them. The ERC Analysis website allows researchers to exploit genome-wide datasets to infer novel genes in any biological function and to explore deep evolutionary connections between distinct pathways and complexes. The website provides five analytical methods, graphical output, statistical support and access to an increasing number of taxonomic groups. AVAILABILITY AND IMPLEMENTATION: Analyses and data at http://csb.pitt.edu/erc_analysis/ CONTACT: [email protected].
Nicholas W. Wolfe, Nathan L. Clark
Bioinform.1