EDBT 2026 Demo / reviewers in the wild / expert
Christian J. Michel
dblp:12/1298
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 3 · 2 first-authorApplied, 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.
| Theoretical computer science
1 paper |
Combinatorics and discrete mathematics · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
genetic code |
0.0 | 1 | 2012 | A classification of 20-trinucleotide circular codes · Inf. Comput. 2012 |
Bioinformatics and computational biology › molecular evolution
molecular evolution simulation |
0.0 | 1 | 1992 | Analysis of gene evolution: the software AGE · Comput. Appl. Biosci. 1992 |
Methods — techniques the papers use, named apart from their topics
markov model · 0.0fourier transformation · 0.0autocorrelation analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | A classification of 20-trinucleotide circular codes
Christian J. Michel, Giuseppe Pirillo, Mario A. Pirillo |
Inf. Comput. | 1 |
| 2008 | A relation between trinucleotide comma-free codes and trinucleotide circular codes
Christian J. Michel, Giuseppe Pirillo, Mario A. Pirillo |
Theor. Comput. Sci. | 1 |
| 1995 | A Prossible Code in the Genetic Code
Didier Arquès, Christian J. Michel |
STACS | 2 |
| 1992 | Analysis of gene evolution: the software AGEabstractThe software AGE (Analysis of Gene Evolution) has been developed both to study a genetic reality, i.e. the identification of statistical properties in genes (e.g. periodicities), and to simulate this observed genetic reality, by models of molecular evolution. AGE has two types of models: (i) models of sequence creation from oligonucleotides: concatenation model in series of an oligonucleotide, independent (or Markov) mixing model of oligonucleotides according to given probabilities (or a Markov matrix); (ii) models of sequence evolution from created sequences: insertion/deletion process of (mono,di,tri)nucleotides, base mutation process. The study of a reality and the development of simulation models are based on several new algorithms: approximated simulation and exact calculus to compute various autocorrelation functions, Fourier transformation of autocorrelation curves, recognition of a curve form, etc. AGE is implemented on IBM or compatible microcomputers and can be used by biologists without any computer knowledge to identify statistical properties in their newly determined DNA sequence and to explain them by models of molecular evolution. Didier Arquès, Christian J. Michel, K. Orieux |
Comput. Appl. Biosci. | 2 |