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P. Jona

dblp:96/3002 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—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 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › network bioinformatics › biological network analysis › network topology analysis
network motif discovery
0.112007
DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networks · Bioinform. 2007
Bioinformatics and computational biology
network randomization
0.012007
DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networks · Bioinform. 2007
Bioinformatics and computational biology › network bioinformatics › biological network analysis
network topology analysis
0.012007
DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networks · Bioinform. 2007

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

importance sampling monte carlo · 0.1
YearPublicationVenuePosition
2007 DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networks
abstract
Abstract Motivation: Transcription networks, and other directed networks can be characterized by some topological observables (e.g. network motifs), that require a suitable randomized network ensemble, typically with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Monte Carlo developed by (Chen et al.). Availability: The algorithm is available at http://wwwteor.mi.infn.it/~bassetti/downloads.html Contact: [email protected] and [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
Diana Fusco, Bruno Bassetti, P. Jona, Marco Cosentino Lagomarsino
Bioinform.3