Demo Data from EDBT 2026 / reviewers in the wild / expert
Yuri Kravatsky
dblp:212/0129
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0002-2499-3428ORCID · reported
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein structure prediction › protein-protein docking
docking model quality assessment |
0.3 | 1 | 2018 | Meta-server for automatic analysis, scoring and ranking of docking models · Bioinform. 2018 |
Bioinformatics and computational biology › protein structure prediction
protein-protein docking |
0.3 | 1 | 2018 | Meta-server for automatic analysis, scoring and ranking of docking models · Bioinform. 2018 |
Bioinformatics and computational biology › protein-protein interaction prediction
interaction site prediction |
0.1 | 1 | 2018 | Meta-server for automatic analysis, scoring and ranking of docking models · Bioinform. 2018 |
Bioinformatics and computational biology
protein-protein interaction prediction |
0.1 | 1 | 2018 | Meta-server for automatic analysis, scoring and ranking of docking models · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
scoring function · 0.3meta-server · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Meta-server for automatic analysis, scoring and ranking of docking modelsabstractMOTIVATION: Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling. RESULTS: Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor-ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor-ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor-ligand complexes. AVAILABILITY: http://qasdom.eimb.ru. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Anastasya A. Anashkina, Yuri Kravatsky, Eugene N. Kuznetsov, Alexander A. Makarov, Alexei A. Adzhubei |
Bioinform. | 2 |