Demo Data from VLDB 2026 / reviewers in the wild / expert
Alexei A. Adzhubei
dblp:90/2512
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0002-5000-1373ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 4 · 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
3 papers |
Bioinformatics and computational biology · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data models and query languages · 100% |
Topics — the 10 heaviest of 10, 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 › genome annotation
functional region identification |
0.2 | 1 | 2013 | SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules · Bioinform. 2013 |
Bioinformatics and computational biology
protein sequence analysis |
0.2 | 1 | 2013 | SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules · Bioinform. 2013 |
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 |
Bioinformatics and computational biology › sequence analysis › motif discovery
protein motif discovery |
0.0 | 1 | 2013 | SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rules · Bioinform. 2013 |
Bioinformatics and computational biology
biological database |
0.0 | 1 | 2001 | MDB: a database system utilizing automatic construction of modules and STAR-derived universal language · Bioinform. 2001 |
Bioinformatics and computational biology › structural bioinformatics
protein structure database |
0.0 | 1 | 2001 | MDB: a database system utilizing automatic construction of modules and STAR-derived universal language · Bioinform. 2001 |
Data models and query languages
data description language |
0.0 | 1 | 2001 | MDB: a database system utilizing automatic construction of modules and STAR-derived universal language · Bioinform. 2001 |
Methods — techniques the papers use, named apart from their topics
scoring function · 0.3meta-server · 0.3scaled profiles · 0.2decoy sequences · 0.2amino acid composition · 0.2dictionary-based data design · 0.1automatic relational database construction · 0.1
| 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. | 5 |
| 2013 | SAPA tool: finding protein regions by combination of amino acid composition, scaled profiles, patterns and rulesabstractSUMMARY: Functional modules within protein sequences are often extracted by consensus sequence patterns representing a linear motif; however, other functional regions may only be described by combined features such as amino acid composition, profiles of amino acid properties and randomly distributed short sequence motifs. If only a small number of functional examples are well characterized, the researcher needs a tool to extract similar sequences for further investigation. AVAILABILITY AND IMPLEMENTATION: We provide the web application 'SAPA tool', which allows the user to search with combined properties, ranks the extracted target regions by an integrated score, estimates false discovery rates by using decoy sequences and provides them as a sequence file or spreadsheet. Source code, user manual and the web application implemented in Perl, HTML, CSS and JavaScript and running on Apache are freely available at http://sapa-tool.uio.no/sapa/ Josef Maier, Alexei A. Adzhubei, Wolfgang Egge-Jacobsen |
Bioinform. | 2 |
| 2006 | preAssemble: a tool for automatic sequencer trace data processingabstractBACKGROUND: Trace or chromatogram files (raw data) are produced by automatic nucleic acid sequencing equipment or sequencers. Each file contains information which can be interpreted by specialised software to reveal the sequence (base calling). This is done by the sequencer proprietary software or publicly available programs. Depending on the size of a sequencing project the number of trace files can vary from just a few to thousands of files. Sequencing quality assessment on various criteria is important at the stage preceding clustering and contig assembly. Two major publicly available packages--Phred and Staden are used by preAssemble to perform sequence quality processing. RESULTS: The preAssemble pre-assembly sequence processing pipeline has been developed for small to large scale automatic processing of DNA sequencer chromatogram (trace) data. The Staden Package Pregap4 module and base-calling program Phred are utilized in the pipeline, which produces detailed and self-explanatory output that can be displayed with a web browser. preAssemble can be used successfully with very little previous experience, however options for parameter tuning are provided for advanced users. preAssemble runs under UNIX and LINUX operating systems. It is available for downloading and will run as stand-alone software. It can also be accessed on the Norwegian Salmon Genome Project web site where preAssemble jobs can be run on the project server. CONCLUSION: preAssemble is a tool allowing to perform quality assessment of sequences generated by automatic sequencing equipment. preAssemble is flexible since both interactive jobs on the preAssemble server and the stand alone downloadable version are available. Virtually no previous experience is necessary to run a default preAssemble job, on the other hand options for parameter tuning are provided. Consequently preAssemble can be used as efficiently for just several trace files as for large scale sequence processing. Alexei A. Adzhubei, Jon K. Laerdahl, Anna V. Vlasova |
BMC Bioinform. | 1 |
| 2001 | MDB: a database system utilizing automatic construction of modules and STAR-derived universal languageabstractMOTIVATION: The value of information greatly increases if stored in databases. The objective was to construct a multi-purpose database system primarily designed to store and provide access to three-dimensional structures of biological molecules including theoretical models. RESULTS: A dictionary defining data format and structure for three-dimensional models of biological molecules (MDB dictionary) was developed. The dictionary was written using universal, standardized data description language. This language can be applied to describe data with no restrictions on their origin or type, including metadata. Thus both the data definitions (format) and database descriptions are created using the uniform language and processed with universal software. A database and data design technique that allowed use of dictionaries to automatically construct relational databases was developed. This technique was employed to construct the MDB database system. Data design developed and applied in the MDB project makes it possible to carry out data curation utilizing the database engine to identify errors. It also allows storage and query of data at different levels of consistency with the standard format specifications, i.e. both the correctly formatted data, and data that requires further curation. AVAILABILITY: The MDB dictionary is available at http://www.gwer.ch/proteinstructure/mdb and as part of the PDB resources at http://pdb.rutgers.edu/mmcif/. Eugenia Migliavacca, Alexei A. Adzhubei, Manuel C. Peitsch |
Bioinform. | 2 |