Name
Abstract | ||
|---|---|---|
Hidden Markov models (HMMs) axe effective tools to detect series of statistically homogeneous structures, but they are not well suited to analyse complex structures. Numerous methodological difficulties are encountered when using HMMs to segregate genes from transposons or retroviruses, or to determine the isochore classes of genes. The aim of this paper is to analyse these methodological difficulties, and to suggest new tools for the exploration of genome data. We show that HMMs can be used to analyse complex genes structures with bell-shaped distributed lengths, modelling them by macro-states. Our data processing method, based on discrimination between macro-states, allows to reveal several specific characteristics of intronless genes, and a break in the homogeneity of the initial coding exons. This potential use of markovian models to help in data exploration seems to have been underestimated until now, and one aim of our paper is to promote this use of Markov modelling. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1142/S0129054108005516 | INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE |
Keywords | Field | DocType |
gene structure,macro-state,hmm,g + c content,data processing,hidden markov model,complex structure | Genome,Data mining,Data processing,Markov process,Gene,Homogeneous,Markov chain,Coding (social sciences),Artificial intelligence,Hidden Markov model,Mathematics,Machine learning | Conference |
Volume | Issue | ISSN |
19 | 1 | 0129-0541 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christelle Melo de Lima | 1 | 0 | 0.68 |
| Laurent Gueguen | 2 | 13 | 2.83 |
| Christian Gautier | 3 | 138 | 28.07 |
| Didier Piau | 4 | 27 | 3.22 |