Demo Data from EDBT 2026 / reviewers in the wild / expert
Kelly Brock
dblp:241/2064
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › molecular evolution
co-evolutionary analysis |
0.4 | 1 | 2019 | The EVcouplings Python framework for coevolutionary sequence analysis · Bioinform. 2019 |
Bioinformatics and computational biology
protein structure prediction |
0.4 | 1 | 2019 | The EVcouplings Python framework for coevolutionary sequence analysis · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
sequence alignment · 0.4direct coupling analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | The EVcouplings Python framework for coevolutionary sequence analysisabstractSUMMARY: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings. Thomas A. Hopf, Anna G. Green, Benjamin Schubert, Sophia Mersmann, Charlotta Schärfe, John Ingraham, Ágnes Tóth-Petróczy, Kelly Brock, Adam J. Riesselman, Perry Palmedo, Chan Kang, Robert P. Sheridan, Eli J. Draizen, Christian Dallago, Chris Sander, Debora S. Marks |
Bioinform. | 8 |