Demo Data from EDBT 2026 / reviewers in the wild / expert
Bruno Bassetti
dblp:82/915
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › network bioinformatics › biological network analysis › network topology analysis
network motif discovery |
0.1 | 1 | 2007 | 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 |
0.0 | 1 | 2007 | 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 |
0.0 | 1 | 2007 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | DIA-MCIS: an importance sampling network randomizer for network motif discovery and other topological observables in transcription networksabstractAbstract 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. | 2 |