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Cette thèse présente le développement, en vue de l'analyse statistique des séquences d'ADN, de nouveaux modèles permettant de prendre en compte l'hétérogénéité de ces séquences : les chaînes de Markov régulées (DMM pour drifting Markov model). Afin d'éviter l'homogénéité supposé par les modèles de Markov et de Markov cachés, nous permettons à la matrice de transition de varier du début à la fin de la séquence. A chaque position, nous avons une matrice de transition différente. Ces modèles peuvent être vus comme une alternative mais aussi comme un outil complémentaire aux modèles de Markov cachés. Nous avons considéré des dérives polynomiales ainsi que des dérives par splines polynomiales. Nous avons estimé nos modèles de multiples manières puis évalué la qualité de ces estimateurs avant de les utiliser en vue d'applications telle la recherche de mots exceptionnels. Nous avons mis en oeuvre le software DRIMM, dédié à l'estimation de nos modèles. / This document propose the conception, in the way of statistical analysis of DNA sequences, of new models which permit to take into account the heterogeneity of these sequences : the drifting Markov models (DMM). In order to avoid homogeneity of Markov models or hidden Markov models, we allow the transition matrix to vary from the beginning to the end of the sequence. At each position, we obtain a different transition matrix. DMM can be seen as a competitive model to the HMM one but it over all can be understood as a complementary tool: the hidden models of an HMM, usually fixed Markov chains can be replaced by DMM. Along this work, we consider polynomial drift or drift by polynomial splines. We estimate our models by different ways, evaluate their qualities and used them in biological applications such as the search of rare words. We develop the software DRIMM, dedicated to estimation of DMM.

Yeung, Hau Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 59-63). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.7 / Chapter 2 --- Work Flows and Kernel Methods --- p.9 / Chapter 2.1 --- Work Flows --- p.9 / Chapter 2.2 --- Frequency Vector --- p.11 / Chapter 2.3 --- Motivation for Kernel Based Distance --- p.12 / Chapter 2.3.1 --- Similarity between sequences --- p.13 / Chapter 2.3.2 --- Distance between sequences --- p.14 / Chapter 2.4 --- Kernels for DNA Sequence --- p.15 / Chapter 2.4.1 --- Kernels based on evolution model --- p.15 / Chapter 2.4.2 --- Kernels based on empirical data --- p.17 / Chapter 2.5 --- Kernels for Peptide Sequence --- p.18 / Chapter 3 --- Dataset for DNA Sequence and Results --- p.25 / Chapter 3.1 --- Dataset and Goal --- p.25 / Chapter 3.1.1 --- Mitochondrial DNA dataset --- p.26 / Chapter 3.1.2 --- 18S ribosomal RNA --- p.28 / Chapter 3.2 --- Results --- p.28 / Chapter 4 --- Dataset for Peptide Sequence and Results --- p.35 / Chapter 4.1 --- Dataset and Goal --- p.36 / Chapter 4.2 --- Classification and Evaluation Methods --- p.39 / Chapter 4.2.1 --- Partition of training and testing datasets --- p.39 / Chapter 4.2.2 --- Classification methods --- p.40 / Chapter 4.3 --- Results --- p.45 / Chapter 4.3.1 --- KNN performs better than the FDSM --- p.45 / Chapter 4.3.2 --- BLOSUM62 performs best and window length not important --- p.46 / Chapter 4.3.3 --- Distance formula (2.4) performs better --- p.49 / Chapter 5 --- Discussion --- p.51 / Chapter 5.1 --- Sequence Length and Window Length --- p.51 / Chapter 5.2 --- Possible Kernels --- p.52 / Chapter 5.3 --- Distance Formulae --- p.53 / Chapter 5.4 --- Protein Structural Problem --- p.54 / Chapter 6 --- Appendix --- p.55 / Chapter 6.1 --- Kernel for Peptide Sequences --- p.55 / Bibliography --- p.59

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

The aim of this project has been to develop a new method that can be used for optimising the sequences of operations in flexible manufacturing cells. The method combines existing methods and available software tools from three research areas, optimisation, flexibility and virtual manufacturing. This method is named SIMBOSeer and it combines genetic algorithms in discrete event simulation together with continuous robot simulation. SIMBOSeer has been implemented at an existing workcell at Volvo Powertrain, Skövde, and it has successfully shown to be able to find possible improvements of the sequences of operations. SIMBOSeer has shown to be a successful combination of different virtual manufacturing tools. It has a great ability to find an optimal solution of the sequence of operations in a flexible manufacturing cell. SIMBOSeer not only gives a fast answer on how to produce different variants it also offers the possibility to generate the robot code automatically.

When timing a fluent sequence of either movement or speech, evidence suggests that people consider the amount of time available during the production of a first unit to prepare a second unit, evident in a reverse length effect for response onset of the sequence (e.g., Griffin, 2003; Ketelaars, Garry, and Franks, 1997). The current study investigated whether such considerations extend to sequences composed of both speech and movement units. Participants named an object and manually traced a course to produce a fluent sequence. Duration of the first production was manipulated. If preparation modulation operates over speech and movement units within the same sequence, sequence onset will be earlier when the first unit is short in production duration as opposed to long. In general, participants began sequences later when production of first unit did not exceed preparation time of the second unit. This finding provides support for preparation.

Knowledge of phylogenetic relationships among taxa is essential for comparative and evolutionary genomic research. Here, we report reconstruction of the phylogenetic tree of the genus Gossypium containing cultivated cottons of importance in agriculture by using variation of nuclear repetitive DNA sequences. Genomic DNA was isolated from 87 available accessions of 35 species representing all eight basic genome groups of the genus Gossypium and analyzed to infer phylogeny of the genus and genome origin of its polyploid species. Twenty-two interspersed repeated sequence clones derived from G. hirsutum, each representing a repeated sequence family, were hybridized to the genomic DNA of the 35 species, respectively. Southern hybridization showed that 15 of the repetitive DNA sequences could be detected in all of the eight diploid genome groups, five were A genome-specific, and two were detected in some of the non D-genome groups. A total of 642 major restriction bands of repeated sequences were used for phylogenetic analysis of the species. A phylogenetic tree of the species was constructed, based on the parsimony method and evaluated by the bootstrap approach. The tree was consistent with those previously constructed with different methods in major clades in which the genealogical lineages of species are largely congruent with genome designations and geographical distribution; but significantly different branching among some of the species was observed. These results not only further confirm the previously phylogenetic analysis of the species and the utility of repetitive DNA sequences for phylogenetic analysis of the genus Gossypium, but also provide new insights into the phylogeny of the genus.

Thesis (doctoral)--K. Technischen Hochschule zu München, 1913. / Includes bibliographical references.

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains iii, 27 p. Includes abstract. Includes bibliographical references (p. 26-27).