Christian J. Michel

dblp:12/1298 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genetic code
0.012012
A classification of 20-trinucleotide circular codes · Inf. Comput. 2012
Bioinformatics and computational biology › molecular evolution
molecular evolution simulation
0.011992
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
YearPublicationVenuePosition
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
STACS2
1992 Analysis of gene evolution: the software AGE
abstract
The 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