Demo Data from EDBT 2026 / reviewers in the wild / expert
L. Lizana
dblp:39/7592
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2009
—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 › systems biology
metabolic network analysis |
0.1 | 1 | 2009 | Pathway identification by network pruning in the metabolic network of Escherichia coli · Bioinform. 2009 |
Bioinformatics and computational biology › systems bioinformatics › pathway analysis
pathway discovery |
0.1 | 1 | 2009 | Pathway identification by network pruning in the metabolic network of Escherichia coli · Bioinform. 2009 |
Bioinformatics and computational biology
systems biology |
0.1 | 1 | 2009 | Pathway identification by network pruning in the metabolic network of Escherichia coli · Bioinform. 2009 |
Methods — techniques the papers use, named apart from their topics
network analysis · 0.1graph pruning · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Pathway identification by network pruning in the metabolic network of Escherichia coliabstractMOTIVATION: All metabolic networks contain metabolites, such as ATP and NAD, known as currency metabolites, which take part in many reactions. These are often removed in the study of these networks, but no consensus exists on what actually constitutes a currency metabolite, and it is also unclear how these highly connected nodes contribute to the global structure of the network. RESULTS: In this article, we analyse how the Escherichia coli metabolic network responds to pruning in the form of sequential removal of metabolites with highest degree. As expected this leads to network fragmentation, but the process by which it occurs suggests modularity and long-range correlations within the network. We find that the pruned networks contain longer paths than the random expectation, and that the paths that survive the pruning also exhibit a lower cost (number of involved metabolites) compared with random paths in the full metabolic network. Finally we confirm that paths detected by pruning overlap with known metabolic pathways. We conclude that pruning reveals functional pathways in metabolic networks, where currency metabolites may be seen as ingredients in a well-balanced soup in which main metabolic production lines are immersed. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Philip Gerlee, L. Lizana, Kim Sneppen |
Bioinform. | 2 |