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Joachim Nöthen

dblp:130/1058 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—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 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › network bioinformatics › biological network analysis › molecular network analysis
biochemical network analysis
0.212013
MonaLisa - visualization and analysis of functional modules in biochemical networks · Bioinform. 2013
Bioinformatics and computational biology › network bioinformatics › biological network analysis
network visualization
0.212013
MonaLisa - visualization and analysis of functional modules in biochemical networks · Bioinform. 2013

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

t-invariants · 0.2petri net analysis · 0.2minimal cut sets · 0.2
YearPublicationVenuePosition
2013 MonaLisa - visualization and analysis of functional modules in biochemical networks
abstract
SUMMARY: Structural modeling of biochemical networks enables qualitative as well as quantitative analysis of those networks. Automated network decomposition into functional modules is a crucial point in network analysis. Although there exist approaches for the analysis of networks, there is no open source tool available that combines editing, visualization and the computation of steady-state functional modules. We introduce a new tool called MonaLisa, which combines computation and visualization of functional modules as well as an editor for biochemical Petri nets. The analysis techniques allow for network decomposition into functional modules, for example t-invariants (elementary modes), maximal common transition sets, minimal cut sets and t-clusters. The graphical user interface provides various functionalities to construct and modify networks as well as to visualize the results of the analysis. AVAILABILITY AND IMPLEMENTATION: MonaLisa is licensed under the Artistic License 2.0. It is freely available at http://www.bioinformatik.uni-frankfurt.de/software.html. MonaLisa requires at least Java 6 and runs under Linux, Microsoft Windows and Mac OS.
Jens Einloft, Jörg Ackermann 0001, Joachim Nöthen, Ina Koch
Bioinform.3