Demo Data from VLDB 2026 / reviewers in the wild / expert
Jan Tavernier
dblp:199/3434
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
0000-0002-7609-6462ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2
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
2 papers |
Bioinformatics and computational biology · 75% Computational science and engineering · 25% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
proteomics |
0.6 | 2 | 2017 | sfinx: an R package for the elimination of false positives from affinity purification-mass spectrometry datasets · Bioinform. 2017 MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform · Bioinform. 2017 |
Computational science and engineering
false positive filtering |
0.3 | 1 | 2017 | sfinx: an R package for the elimination of false positives from affinity purification-mass spectrometry datasets · Bioinform. 2017 |
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction analysis |
0.3 | 1 | 2017 | MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform · Bioinform. 2017 |
Methods — techniques the papers use, named apart from their topics
straightforward filtering index · 0.3meta-analysis · 0.3data management · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platformabstractSummary: Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments. Availability and Implementation: MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online. Surya Gupta, Veronic De Puysseleyr, José Van der Heyden, Davy Maddelein, Irma Lemmens, Sam Lievens, Sven Degroeve, Jan Tavernier, Lennart Martens |
Bioinform. | 8 |
| 2017 | sfinx: an R package for the elimination of false positives from affinity purification-mass spectrometry datasetsabstractSUMMARY: We describe sfinx, an R package providing access to the straightforward filtering index (SFINX) for the separation of true positive from false positive protein interactions in affinity purification - mass spectrometry datasets. This package maintains the reliability and user-friendliness of the SFINX web site interface but is faster, unlimited in input size, and can be run locally within R. AVAILABILITY AND IMPLEMENTATION: The sfinx R package is available for download at the comprehensive R archive network (CRAN) https://cran.r-project.org/web/packages/sfinx/ under the Apache License 2.0. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Kevin Titeca, Pieter Meysman, Kris Laukens, Lennart Martens, Jan Tavernier, Sven Eyckerman |
Bioinform. | 5 |