Demo Data from VLDB 2026 / reviewers in the wild / expert
Dimitrij Surmeli
dblp:54/4135
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Applied, 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 · 70% Computational science and engineering · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering
benchmark dataset |
0.1 | 1 | 2005 | MIPS bacterial genomes functional annotation benchmark dataset · Bioinform. 2005 |
Bioinformatics and computational biology › genome annotation
functional annotation |
0.1 | 1 | 2005 | MIPS bacterial genomes functional annotation benchmark dataset · Bioinform. 2005 |
Bioinformatics and computational biology
genome annotation |
0.1 | 1 | 2005 | MIPS bacterial genomes functional annotation benchmark dataset · Bioinform. 2005 |
Bioinformatics and computational biology › protein sequence analysis
protein sequence annotation |
0.0 | 1 | 2005 | MIPS bacterial genomes functional annotation benchmark dataset · Bioinform. 2005 |
Methods — techniques the papers use, named apart from their topics
sequence similarity · 0.1interpro domain composition · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | MIPS bacterial genomes functional annotation benchmark datasetabstractMOTIVATION: Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult. RESULTS: The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation. AVAILABILITY: BFAB is available at http://mips.gsf.de/proj/bfab Igor V. Tetko, Barbara Brauner, Irmtraud Dunger, Goar Frishman, Corinna Montrone, Gisela Fobo, Andreas Ruepp, Alexey V. Antonov, Dimitrij Surmeli, Hans-Werner Mewes |
Bioinform. | 9 |
| 1994 | Gated dipoles for operant conditioning
Paolo Gaudiano, Dimitrij Surmeli, Frank D. M. Wilson |
Neural Networks | 2 |