Demo Data from EDBT 2026 / reviewers in the wild / expert
Gearoid Fox
dblp:141/5370
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 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
1 paper |
Bioinformatics and computational biology · 100% |
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 › multiple sequence alignment › alignment quality assessment
alignment benchmark |
0.2 | 1 | 2016 | Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments · Bioinform. 2016 |
Bioinformatics and computational biology
multiple sequence alignment |
0.2 | 1 | 2016 | Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments · Bioinform. 2016 |
Bioinformatics and computational biology
protein structure prediction |
0.2 | 1 | 2016 | Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
de novo protein structure prediction · 0.2chained guide trees · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Using de novo protein structure predictions to measure the quality of very large multiple sequence alignmentsabstractMOTIVATION: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data. RESULTS: We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA methods using this benchmark and verify a recent result showing that chained guide trees increase the accuracy of progressive alignment packages on datasets with thousands of proteins. AVAILABILITY AND IMPLEMENTATION: Benchmark data and scripts are available for download at http://www.bioinf.ucd.ie/download/ContTest.tar.gz CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Gearoid Fox, Fabian Sievers, Desmond G. Higgins |
Bioinform. | 1 |