Demo Data from EDBT 2026 / reviewers in the wild / expert
G. Fichant
dblp:17/5841
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1987
—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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › molecular evolution
codon usage analysis |
0.0 | 1 | 1987 | Statistical method for predicting protein coding regions in nucleic acid sequences · Comput. Appl. Biosci. 1987 |
Bioinformatics and computational biology
genome annotation |
0.0 | 1 | 1987 | Statistical method for predicting protein coding regions in nucleic acid sequences · Comput. Appl. Biosci. 1987 |
Bioinformatics and computational biology › genome annotation
protein coding region prediction |
0.0 | 1 | 1987 | Statistical method for predicting protein coding regions in nucleic acid sequences · Comput. Appl. Biosci. 1987 |
Bioinformatics and computational biology
sequence analysis |
0.0 | 1 | 1987 | Statistical method for predicting protein coding regions in nucleic acid sequences · Comput. Appl. Biosci. 1987 |
Methods — techniques the papers use, named apart from their topics
correspondence analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1987 | Statistical method for predicting protein coding regions in nucleic acid sequencesabstractProtein coding regions of a genome fragment can be mathematically predicted by studying variations in the statistical properties or by searching the signals characteristic of the junctions between the coding and non-coding regions. We propose here a new statistical method using correspondence analysis. This method does not use any reference codon set but takes into account the codon usage homogeneity along the studied genome fragment. Comparison with previously published methods especially the 'codon usage method' of Staden has been made, and two examples are presented here. Applications to analysis of prokaryotic operon and eukaryotic split genes are also discussed. Use of the method has also shown two structures not previously described: i) in the human prt gene, a strong triplet structure exists in a non-coding region; ii) in the human tp-a codon usage is not uniform between the different exons. G. Fichant, Christian Gautier |
Comput. Appl. Biosci. | 1 |