Literatura académica sobre el tema "Bio-statistique"
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Artículos de revistas sobre el tema "Bio-statistique"
Giroux, Élodie. "Définir objectivement la santé : une évaluation du concept bio statistique de Boorse à partir de l'épidémiologie moderne". Revue philosophique de la France et de l'étranger 134, n.º 1 (2009): 35. http://dx.doi.org/10.3917/rphi.091.0035.
Texto completoClément, Léa y Barbara Testoni. "Le dysfonctionnement mitochondrial, nouvelle cible thérapeutique pour restaurer les réponses immunitaires épuisées contre le VHB". médecine/sciences 38, n.º 2 (febrero de 2022): 223–26. http://dx.doi.org/10.1051/medsci/2022011.
Texto completoBraissand, Nicolas y Isabelle Coste. "Thérapies ciblées et immunothérapies dans le mélanome". médecine/sciences 39, n.º 11 (noviembre de 2023): 889–92. http://dx.doi.org/10.1051/medsci/2023126.
Texto completoAndrieu, Nathan y Nathalie Bendriss-Vermare. "Immunothérapie et thérapies ciblées, une combinaison d’avenir dans la lutte contre le cancer". médecine/sciences 34, n.º 10 (octubre de 2018): 872–75. http://dx.doi.org/10.1051/medsci/2018217.
Texto completoEstavoyer, Benjamin y Saidi Soudja. "Le microbiote intestinal de souris, un enjeu majeur dans la reproductibilité des résultats des modèles in vivo". médecine/sciences 34, n.º 6-7 (junio de 2018): 609–11. http://dx.doi.org/10.1051/medsci/20183406027.
Texto completoThevin, Valentin y Saidi Soudja. "Le dialogue entre les cellules souches intestinales et les lymphocytes T CD4+ module l’homéostasie des cellules souches". médecine/sciences 36, n.º 1 (enero de 2020): 69–72. http://dx.doi.org/10.1051/medsci/2019175.
Texto completoFijalkow, Yankel. "Hygiene, Population Sciences and Population Policy: a Totalitarian Menace?" Contemporary European History 8, n.º 3 (noviembre de 1999): 451–72. http://dx.doi.org/10.1017/s0960777399003082.
Texto completoTesis sobre el tema "Bio-statistique"
Douib, Ameur. "Algorithmes bio-inspirés pour la traduction automatique statistique". Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0005/document.
Texto completoDifferent components of statistical machine translation systems are considered as optimization problems. Indeed, the learning of the translation model, the decoding and the optimization of the weights of the log-linear function are three important optimization problems. Knowing how to define the right algorithms to solve them is one of the most important tasks in order to build an efficient translation system. Several optimization algorithms are proposed to deal with decoder optimization problems. They are combined to solve, on the one hand, the decoding problem that produces a translation in the target language for each source sentence, on the other hand, to solve the problem of optimizing the weights of the combined scores in the log-linear function to fix the translation evaluation function during the decoding. The reference system in statistical translation is based on a beam-search algorithm for the decoding, and a line search algorithm for optimizing the weights associated to the scores. We propose a new statistical translation system with a decoder entirely based on genetic algorithms. Genetic algorithms are bio-inspired optimization algorithms that simulate the natural process of evolution of species. They allow to handle a set of solutions through several iterations to converge towards optimal solutions. This work allows us to study the efficiency of the genetic algorithms for machine translation. The originality of our work is the proposition of two algorithms: a genetic algorithm, called GAMaT, as a decoder for a phrase-based machine translation system, and a second genetic algorithm, called GAWO, for optimizing the weights of the log-linear function in order to use it as a fitness function for GAMaT. We propose also, a neuronal approach to define a new fitness function for GAMaT. This approach consists in using a neural network to learn a function that combines several scores, which evaluate different aspects of a translation hypothesis, previously combined in the log-linear function, and that predicts the BLEU score of this translation hypothesis. This work allowed us to propose a new machine translation system with a decoder entirely based on genetic algorithms
Douib, Ameur. "Algorithmes bio-inspirés pour la traduction automatique statistique". Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0005.
Texto completoDifferent components of statistical machine translation systems are considered as optimization problems. Indeed, the learning of the translation model, the decoding and the optimization of the weights of the log-linear function are three important optimization problems. Knowing how to define the right algorithms to solve them is one of the most important tasks in order to build an efficient translation system. Several optimization algorithms are proposed to deal with decoder optimization problems. They are combined to solve, on the one hand, the decoding problem that produces a translation in the target language for each source sentence, on the other hand, to solve the problem of optimizing the weights of the combined scores in the log-linear function to fix the translation evaluation function during the decoding. The reference system in statistical translation is based on a beam-search algorithm for the decoding, and a line search algorithm for optimizing the weights associated to the scores. We propose a new statistical translation system with a decoder entirely based on genetic algorithms. Genetic algorithms are bio-inspired optimization algorithms that simulate the natural process of evolution of species. They allow to handle a set of solutions through several iterations to converge towards optimal solutions. This work allows us to study the efficiency of the genetic algorithms for machine translation. The originality of our work is the proposition of two algorithms: a genetic algorithm, called GAMaT, as a decoder for a phrase-based machine translation system, and a second genetic algorithm, called GAWO, for optimizing the weights of the log-linear function in order to use it as a fitness function for GAMaT. We propose also, a neuronal approach to define a new fitness function for GAMaT. This approach consists in using a neural network to learn a function that combines several scores, which evaluate different aspects of a translation hypothesis, previously combined in the log-linear function, and that predicts the BLEU score of this translation hypothesis. This work allowed us to propose a new machine translation system with a decoder entirely based on genetic algorithms
Dortel, Emmanuelle. "Croissance de l'albacore (Thunnus albacares) de l'Océan Indien : de la modélisation statistique à la modélisation bio-énergétique". Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20035/document.
Texto completoSince the early 1960s, the growth of yellowfin has been enjoyed a particular attention both in the research field and for fisheries management. In the Indian Ocean, the management of yellowfin stock, under the jurisdiction of the Indian Ocean Tuna Commission (IOTC), suffers from much uncertainty associated with the growth curve currently considered. In particular, there remain gaps in our knowledge of basic biological and ecological processes regulating growth. Their knowledge is however vital for understanding the stocks productivity and their resilience abilities to fishing pressure and oceanographic changes underway.Through modelling, this study aims to improve current knowledge on the growth of yellowfin population of the Indian Ocean and thus strengthen the scientific advice on the stock status. Whilst most studies on yellowfin growth only rely on one data source, we implemented a hierarchical Bayesian model that exploits various information sources on growth, i.e. direct age estimates obtained through otolith readings, analyzes of modal progressions and individual growth rates derived from mark-recapture experiments, and takes explicitely into account the expert knowledge and the errors associated with each dataset and the growth modelling process. In particular, the growth model was coupled with an ageing error model from repeated otolith readings which significantly improves the age estimates as well as the resulting growth estimates and allows a better assessment of the estimates reliability. The growth curves obtained constitute a major improvement of the growth pattern currently used in the yellowfin stock assessment. They demonstrates that yellowfin exhibits a two-stanzas growth, characterized by a sharp acceleration at the end of juvenile stage. However, they do not provide information on the biological and ecological mechanisms that lie behind the growth acceleration.For a better understanding of factors involved in the acceleration of growth, we implemented a bioenergetic model relying on the principles of Dynamic Energy Budget theory (DEB). Two major assumptions were investigated : (i) a low food availability during juvenile stage in relation with high intra and inter-specific competition and (ii) changes in food diet characterized by the consumption of more energetic prey in older yellowfin. It appears that these two assumption may partially explain the growth acceleration
Hadj, Amor Khaoula. "Classification et inférence de réseaux de gènes à partir de séries temporelles très courtes : application à la modélisation de la mémoire transcriptionnelle végétale associée à des stimulations sonores répétées". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES037.
Texto completoAdvancements in new sequencing technologies have paved the way for accessing dynamic gene expression data on a genome-wide scale. Classical ensemble approaches traditionally used in biology fall short of comprehending the underlying the complex molecular mechanisms. Consequently, developing analytical methods to understand further such data poses a significant challenge for current biology. However, the technical and experimental costs associated with transcriptomic data severely limit the dimension of real datasets and their analytical methods. Throughout my thesis, at the intersection of applied mathematics and plant biology, I focused on implementing an inference method for dynamic regulatory networks tailored to a real and original dataset describing the effect of repeated acoustic stimulations on genes expressions of Arabidopsis thaliana. I proposed a clustering method adapted to very-short time series that groups genes based on temporal variations, adjusting the data dimension for network inference. The comparison of this method with classical methods showed that it was the most suitable for very-short time series with irregular time points. For the network inference, I proposed a model that takes into account intra-class variability and integrates a constant term explicitly describing the external stimulation of the system. The evaluation of these classification and inference methods was conducted on simulated and real-time series data, which established their high performance in terms of accuracy, recall, and prediction error. The implementation of these methods to study the priming of the immune response of Arabidopsis thaliana through repeated sound waves. We demonstrated the formation of a transcriptional memory associated with stimulations, transitioning the plant from a naïve state to a primed and more resistant state within 3 days. This resistant state, maintained by stimulations and transcription factor cascades, enhances the plant's immune resistance by triggering the expression of resistance genes in healthy plants, diversifying the number of genes involved in the immune response, and intensifying the expression of numerous resistance genes. The inference of the network describing the transcriptional memory associated with repeated sound stimulations allowed us to identify the properties conferred to plants. Experimentally validated predictions showed that increasing the frequency between stimulations does not result in a more significant resistance gain, and the transcriptional memory lasts only 1.5 days after the last stimulation
Playe, Benoit. "Méthodes d'apprentissage statistique pour le criblage virtuel de médicament". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM010/document.
Texto completoThe rational drug discovery process has limited success despite all the advances in understanding diseases, and technological breakthroughs. Indeed, the process of drug development is currently estimated to require about 1.8 billion US dollars over about 13 years on average. Computational approaches are promising ways to facilitate the tedious task of drug discovery. We focus in this thesis on statistical approaches which virtually screen a large set of compounds against a large set of proteins, which can help to identify drug candidates for known therapeutic targets, anticipate potential side effects or to suggest new therapeutic indications of known drugs. This thesis is conceived following two lines of approaches to perform drug virtual screening : data-blinded feature-based approaches (in which molecules and proteins are numerically described based on experts' knowledge), and data-driven feature-based approaches (in which compounds and proteins numerical descriptors are learned automatically from the chemical graph and the protein sequence). We discuss these approaches, and also propose applications of virtual screening to guide the drug discovery process
Massé, Pierre-Yves. "Autour De L'Usage des gradients en apprentissage statistique". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS568/document.
Texto completoWe prove a local convergence theorem for the classical dynamical system optimization algorithm called RTRL, in a nonlinear setting. The rtrl works on line, but maintains a huge amount of information, which makes it unfit to train even moderately big learning models. The NBT algorithm turns it by replacing these informations by a non-biased, low dimension, random approximation. We also prove the convergence with arbitrarily close to one probability, of this algorithm to the local optimum reached by the RTRL algorithm. We also formalize the LLR algorithm and conduct experiments on it, on synthetic data. This algorithm updates in an adaptive fashion the step size of a gradient descent, by conducting a gradient descent on this very step size. It therefore partially solves the issue of the numerical choice of a step size in a gradient descent. This choice influences strongly the descent and must otherwise be hand-picked by the user, following a potentially long research
Blum, Michael G. B. "Statistique bayésienne et applications en génétique des populations". Habilitation à diriger des recherches, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00766196.
Texto completoRamstein, Gérard. "Application de techniques de fouille de données en Bio-informatique". Habilitation à diriger des recherches, Université de Nantes, 2012. http://tel.archives-ouvertes.fr/tel-00706566.
Texto completoMoulin, Serge. "Use of data analysis techniques to solve specific bioinformatics problems". Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD049/document.
Texto completoNowadays, the quantity of sequenced genetic data is increasing exponentially under the impetus of increasingly powerful sequencing tools, such as high-throughput sequencing tools in particular. In addition, these data are increasingly accessible through online databases. This greater availability of data opens up new areas of study that require statisticians and bioinformaticians to develop appropriate tools. In addition, constant statistical progress in areas such as clustering, dimensionality reduction, regressions and others needs to be regularly adapted to the context of bioinformatics. The objective of this thesis is the application of advanced statistical techniques to bioinformatics issues. In this manuscript we present the results of our works concerning the clustering of genetic sequences via Laplacian eigenmaps and Gaussian mixture model, the study of the propagation of transposable elements in the genome via a branching process, the analysis of metagenomic data in ecology via ROC curves or the ordinal polytomous regression penalized by the l1-norm
Belkhir, Nacim. "Per Instance Algorithm Configuration for Continuous Black Box Optimization". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS455/document.
Texto completoThis PhD thesis focuses on the automated algorithm configuration that aims at finding the best parameter setting for a given problem or a' class of problem. The Algorithm Configuration problem thus amounts to a metal Foptimization problem in the space of parameters, whosemetaFobjective is the performance measure of the given algorithm at hand with a given parameter configuration. However, in the continuous domain, such method can only be empirically assessed at the cost of running the algorithm on some problem instances. More recent approaches rely on a description of problems in some features space, and try to learn a mapping from this feature space onto the space of parameter configurations of the algorithm at hand. Along these lines, this PhD thesis focuses on the Per Instance Algorithm Configuration (PIAC) for solving continuous black boxoptimization problems, where only a limited budget confessionnalisations available. We first survey Evolutionary Algorithms for continuous optimization, with a focus on two algorithms that we have used as target algorithm for PIAC, DE and CMAFES. Next, we review the state of the art of Algorithm Configuration approaches, and the different features that have been proposed in the literature to describe continuous black box optimization problems. We then introduce a general methodology to empirically study PIAC for the continuous domain, so that all the components of PIAC can be explored in real Fworld conditions. To this end, we also introduce a new continuous black box test bench, distinct from the famous BBOB'benchmark, that is composed of a several multiFdimensional test functions with different problem properties, gathered from the literature. The methodology is finally applied to two EAS. First we use Differential Evolution as'target algorithm, and explore all the components of PIAC, such that we empirically assess the best. Second, based on the results on DE, we empirically investigate PIAC with Covariance Matrix Adaptation Evolution Strategy (CMAFES) as target algorithm. Both use cases empirically validate the proposed methodology on the new black box testbench for dimensions up to100
Libros sobre el tema "Bio-statistique"
Laignelet, Bernard. Analyse statistique et optimisation dans les bio-industries. Paris: Hermann, 1993.
Buscar texto completoZuckerman, Daniel M. Statistical physics of biomolecules: An introduction. Boca Raton: CRC Press/Taylor & Francis, 2010.
Buscar texto completoE, MacCuish Norah, ed. Clustering in bioinformatics and drug discovery. Boca Raton: Taylor & Francis, 2011.
Buscar texto completoThomas, Kurian George. Global data locator. Lanham, Md: Bernan Press, 1997.
Buscar texto completoIntroduction to Statistical Biophysics. CRC, 2009.
Buscar texto completoHeuschling, Xavier. Bibliographie historique de la statistique en Allemagne: Avec une introduction générale. Manuel préparatoire à l\'étude de la statistique. Adamant Media Corporation, 2001.
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