Demo Data from VLDB 2026 / reviewers in the wild / expert
J. Pelc
dblp:228/5331
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
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 · 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
false positive filtering |
0.3 | 1 | 2018 | Bio-knowledge-based filters improve residue-residue contact prediction accuracy · Bioinform. 2018 |
Bioinformatics and computational biology
protein structure prediction |
0.3 | 1 | 2018 | Bio-knowledge-based filters improve residue-residue contact prediction accuracy · Bioinform. 2018 |
Bioinformatics and computational biology › protein structure prediction
residue contact prediction |
0.3 | 1 | 2018 | Bio-knowledge-based filters improve residue-residue contact prediction accuracy · Bioinform. 2018 |
Bioinformatics and computational biology › protein structure prediction
direct coupling analysis |
0.1 | 1 | 2018 | Bio-knowledge-based filters improve residue-residue contact prediction accuracy · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
knowledge-based filtering · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Bio-knowledge-based filters improve residue-residue contact prediction accuracyabstractMotivation: Residue-residue contact prediction through direct coupling analysis has reached impressive accuracy, but yet higher accuracy will be needed to allow for routine modelling of protein structures. One way to improve the prediction accuracy is to filter predicted contacts using knowledge about the particular protein of interest or knowledge about protein structures in general. Results: We focus on the latter and discuss a set of filters that can be used to remove false positive contact predictions. Each filter depends on one or a few cut-off parameters for which the filter performance was investigated. Combining all filters while using default parameters resulted for a test set of 851 protein domains in the removal of 29% of the predictions of which 92% were indeed false positives. Availability and implementation: All data and scripts are available at http://comprec-lin.iiar.pwr.edu.pl/FPfilter/. Supplementary information: Supplementary data are available at Bioinformatics online. Pawel P. Wozniak, J. Pelc, M. Skrzypecki, Gert Vriend, Malgorzata Kotulska |
Bioinform. | 2 |