Demo Data from EDBT 2026 / reviewers in the wild / expert
Volker A. Eyrich
dblp:78/5615
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · unresolved
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 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
benchmark evaluation of prediction tools |
0.0 | 1 | 2001 | EVA: continuous automatic evaluation of protein structure prediction servers · Bioinform. 2001 |
Bioinformatics and computational biology
protein structure prediction |
0.0 | 1 | 2001 | EVA: continuous automatic evaluation of protein structure prediction servers · Bioinform. 2001 |
Methods — techniques the papers use, named apart from their topics
automated evaluation pipeline · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | EVA: continuous automatic evaluation of protein structure prediction serversabstractUNLABELLED: Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. AVAILABILITY: http://cubic.bioc.columbia.edu/eva. CONTACT: [email protected] Volker A. Eyrich, Marc A. Martí-Renom, Dariusz Przybylski, M. S. Madhusudhan 0001, András Fiser, Florencio Pazos, Alfonso Valencia, Andrej Sali, Burkhard Rost |
Bioinform. | 1 |