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Alan E. Barber II

dblp:120/5495 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2012
—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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein analysis
0.112012
Pythoscape: a framework for generation of large protein similarity networks · Bioinform. 2012
Bioinformatics and computational biology › biological network › network biology
protein similarity network
0.112012
Pythoscape: a framework for generation of large protein similarity networks · Bioinform. 2012
Bioinformatics and computational biology
sequence analysis
0.012012
Pythoscape: a framework for generation of large protein similarity networks · Bioinform. 2012

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

representative node compression · 0.1network filtering · 0.1
YearPublicationVenuePosition
2012 Pythoscape: a framework for generation of large protein similarity networks
abstract
Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence-structure-function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.
Alan E. Barber II, Patricia C. Babbitt
Bioinform.1