Literatura científica selecionada sobre o tema "Classification des noeuds"
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Artigos de revistas sobre o assunto "Classification des noeuds"
Ferraz, Antonio. "DÉTECTION À HAUTE RÉSOLUTION SPATIALE DE LA DESSERTE FORESTIÈRE EN MILIEU MONTAGNEUX". Revue Française de Photogrammétrie et de Télédétection 1, n.º 211-212 (6 de dezembro de 2015): 103–17. http://dx.doi.org/10.52638/rfpt.2015.549.
Texto completo da fonteCook, Jesse, e David Plante. "483 Neuroanatomical and Neurofunctional Correlates of Unexplained Excessive Daytime Sleepiness". Sleep 44, Supplement_2 (1 de maio de 2021): A190—A191. http://dx.doi.org/10.1093/sleep/zsab072.482.
Texto completo da fonteJannah, Binti Shofiatul, Iwan Triyuwono, Aji Dedi Mulawarman e Bambang Hariadi. "The Meaning of "Accounting" In a Religious-Based Organization". GATR Journal of Accounting and Finance Review (GATR-AFR) Vol. 6 (2) JULY - SEPTEMBER 2021 6, n.º 2 (29 de setembro de 2021): 78–83. http://dx.doi.org/10.35609/afr.2021.6.2(1).
Texto completo da fonteTeses / dissertações sobre o assunto "Classification des noeuds"
Caudron, Alain. "Classification des noeuds et des entrelacs". Grenoble 2 : ANRT, 1987. http://catalogue.bnf.fr/ark:/12148/cb37603922w.
Texto completo da fonteBon, Michaël. "Prediction de structures secondaires d'ARN avec pseudo-noeuds". Phd thesis, Ecole Polytechnique X, 2009. http://pastel.archives-ouvertes.fr/pastel-00005806.
Texto completo da fonteZeng, Cong. "Classification of RNA Pseudoknots and Comparison of Structure Prediction Methods". Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112127/document.
Texto completo da fonteLots of researches convey the importance of the RNA molecules, as they play vital roles in many molecular procedures. And it is commonly believed that the structures of the RNA molecules hold the key to the discovery of their functions.During the investigation of RNA structures, the researchers are dependent on the bioinformatical methods increasingly. Many in silico methods of predicting RNA secondary structures have emerged in this big wave, including some ones which are capable of predicting pseudoknots, a particular type of RNA secondary structures.The purpose of this dissertation is to try to compare the state-of-the-art methods predicting pseudoknots, and offer the colleagues some insights into how to choose a practical method for the given single sequence. In fact, lots of efforts have been done into the prediction of RNA secondary structures including pseudoknots during the last decades, contributing to many programs in this field. Some challenging questions are raised consequently. How about the performance of each method, especially on a particular class of RNA sequences? What are their advantages and disadvantages? What can we benefit from the contemporary methods if we want to develop new ones? This dissertation holds the confidence in the investigation of the answers.This dissertation carries out quite many comparisons of the performance of predicting RNA pseudoknots by the available methods. One main part focuses on the prediction of frameshifting signals by two methods principally. The second main part focuses on the prediction of pseudoknots which participate in much more general molecular activities.In detail, the second part of work includes 414 pseudoknots, from both the Pseudobase and the Protein Data Bank, and 15 methods including 3 exact methods and 12 heuristic ones. Specifically, three main categories of complexity measurements are introduced, which further divide the 414 pseudoknots into a series of subclasses respectively. The comparisons are carried out by comparing the predictions of each method based on the entire 414 pseudoknots, and the subsets which are classified by both the complexity measurements and the length, RNA type and organism of the pseudoknots.The result shows that the pseudoknots in nature hold a relatively low complexity in all measurements. And the performance of contemporary methods varies from subclass to subclass, but decreases consistently as the complexity of pseudoknots increases. More generally, the heuristic methods globally outperform the exact ones. And the susceptible assessment results are dependent strongly on the quality of the reference structures and the evaluation system. Last but not least, this part of work is provided as an on-line benchmark for the bioinformatics community
Celikkanat, Abdulkadir. "Graph Representation Learning with Random Walk Diffusions". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG030.
Texto completo da fonteGraph Representation Learning aims to embed nodes in a low-dimensional space. In this thesis, we tackle various challenging problems arising in the field. Firstly, we study how to leverage the inherent local community structure of graphs while learning node representations. We learn enhanced community-aware representations by combining the latent information with the embeddings. Moreover, we concentrate on the expressive- ness of node representations. We emphasize exponential family distributions to capture rich interaction patterns. We propose a model that combines random walks with kernelized matrix factorization. In the last part of the thesis, we study models balancing the trade-off between efficiency and accuracy. We propose a scalable embedding model which computes binary node representations
Ah-Pine, Julien. "Sur des aspects algébriques et combinatoires de l'analyse relationnelle : applications en classification automatique, en théorie du choix social et en théorie des tresses". Paris 6, 2007. http://www.theses.fr/2007PA066552.
Texto completo da fonteEl, Haj Abir. "Stochastics blockmodels, classifications and applications". Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2300.
Texto completo da fonteThis PhD thesis focuses on the analysis of weighted networks, where each edge is associated to a weight representing its strength. We introduce an extension of the binary stochastic block model (SBM), called binomial stochastic block model (bSBM). This question is motivated by the study of co-citation networks in a context of text mining where data is represented by a graph. Nodes are words and each edge joining two words is weighted by the number of documents included in the corpus simultaneously citing this pair of words. We develop an inference method based on a variational maximization algorithm (VEM) to estimate the parameters of the modelas well as to classify the words of the network. Then, we adopt a method based on maximizing an integrated classification likelihood (ICL) criterion to select the optimal model and the number of clusters. Otherwise, we develop a variational approach to analyze the given network. Then we compare the two approaches. Applications based on real data are adopted to show the effectiveness of the two methods as well as to compare them. Finally, we develop a SBM model with several attributes to deal with node-weighted networks. We motivate this approach by an application that aims at the development of a tool to help the specification of different cognitive treatments performed by the brain during the preparation of the writing
JEAN, DIT TEYSSIER Loïc. "Équation homologique et classification analytique des germes de champs de vecteurs holomorphes de type noeud-col". Phd thesis, Université Rennes 1, 2003. http://tel.archives-ouvertes.fr/tel-00005387.
Texto completo da fonteJean, dit Teyssier Loïc. "Equation homologique et classification analytique des germes de champs de vecteurs holomorphes de type noeud-col". Rennes 1, 2003. https://tel.archives-ouvertes.fr/tel-00005387.
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