Demo Data from EDBT 2026 / reviewers in the wild / expert
Eric Olo Ndela
dblp:425/2847
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-4241-1161ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
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 › genome annotation
coding region prediction |
0.7 | 1 | 2023 | CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes · Bioinform. 2023 |
Bioinformatics and computational biology › genome annotation
gene prediction |
0.7 | 1 | 2023 | CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes · Bioinform. 2023 |
Bioinformatics and computational biology › genomics
viral genomics |
0.7 | 1 | 2023 | CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
metagenomic read analysis · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomesabstractSUMMARY: Viral genes, that are frequently small genes and/or with large overlaps, are still difficult to predict accurately. To help predict all genes in viral genomes, we provide CodingDiv that detects SNP-level microdiversity of all potential coding regions, using metagenomic reads and/or similar sequences from external databases. Protein coding regions can then be identified as the ones containing more synonymous SNPs than unfavorable nonsynonymous substitutions SNPs. AVAILABILITY AND IMPLEMENTATION: CodingDiv is released under the GPL license. Source code is available at https://github.com/ericolo/codingDiv. The software can be installed and used through a docker container. Eric Olo Ndela, François Enault |
Bioinform. | 1 |