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G. Fichant

dblp:17/5841 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › molecular evolution
codon usage analysis
0.011987
Statistical method for predicting protein coding regions in nucleic acid sequences · Comput. Appl. Biosci. 1987
Bioinformatics and computational biology
genome annotation
0.011987
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
0.011987
Statistical method for predicting protein coding regions in nucleic acid sequences · Comput. Appl. Biosci. 1987
Bioinformatics and computational biology
sequence analysis
0.011987
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
YearPublicationVenuePosition
1987 Statistical method for predicting protein coding regions in nucleic acid sequences
abstract
Protein 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