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Documents  92B15 | enregistrements trouvés : 10

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Probability and Statistics

Differences in disease predisposition or response to treatment can be explained in great part by genomic differences between individuals. This has given birth to precision medicine, where treatment is tailored to the genome of patients. This field depends on collecting considerable amounts of molecular data for large numbers of individuals, which is being enabled by thriving developments in genome sequencing and other high-throughput experimental technologies.
Unfortunately, we still lack effective methods to reliably detect, from this data, which of the genomic features determine a phenotype such as disease predisposition or response to treatment. One of the major issues is that the number of features that can be measured is large (easily reaching tens of millions) with respect to the number of samples for which they can be collected (more usually of the order of hundreds or thousands), posing both computational and statistical difficulties.
In my talk I will discuss how to use biological networks, which allow us to understand mutations in their genomic context, to address these issues. All the methods I will present share the common hypotheses that genomic regions that are involved in a given phenotype are more likely to be connected on a given biological network than not.
Differences in disease predisposition or response to treatment can be explained in great part by genomic differences between individuals. This has given birth to precision medicine, where treatment is tailored to the genome of patients. This field depends on collecting considerable amounts of molecular data for large numbers of individuals, which is being enabled by thriving developments in genome sequencing and other high-throughput ex...

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Mathematics in Science and Technology;Probability and Statistics

In recent years, new pandemic threats have become more and more frequent (SARS, bird flu, swine flu, Ebola, MERS, nCoV...) and analyses of data from the early spread more and more common and rapid. Particular interest is usually focused on the estimation of $ R_{0}$ and various methods, essentially based estimates of exponential growth rate and generation time distribution, have been proposed. Other parameters, such as fatality rate, are also of interest. In this talk, various sources of bias arising because observations are made in the early phase of spread will be discussed and also possible remedies proposed. In recent years, new pandemic threats have become more and more frequent (SARS, bird flu, swine flu, Ebola, MERS, nCoV...) and analyses of data from the early spread more and more common and rapid. Particular interest is usually focused on the estimation of $ R_{0}$ and various methods, essentially based estimates of exponential growth rate and generation time distribution, have been proposed. Other parameters, such as fatality rate, are also of ...

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ISBN 978-90-277-2397-0

The university of western ontario series in philosophy of science , 0038

Localisation : Colloque 1er étage (LOND)

analyse discriminante de groupe multiple # analyse multivariée # biostatistique # cancer # cancérologie # corrélation intraclasse # dérivation échantillon large # effet d'amas # erreur de moindre carrée # estimateur # estimation de rapport impair # estimation de vraissemblance maximum # exploration des grandes bases de données médicales # expérimentation toxicologique # génération de somme # modèle de probabilité de Poisson généralisée # méthode d'estimation pour évaluation parabolique symétrique # méthode de régression # processus du mécanisme des chances # qualité de vie # ramification de statistique # repression logistique multinomiale # récolte des expériences # statistique # table de contingence # traitement de données en biostatistique # épidémiologie # évaluation d'ensemble de réponse # évaluation du mécanisme des chances analyse discriminante de groupe multiple # analyse multivariée # biostatistique # cancer # cancérologie # corrélation intraclasse # dérivation échantillon large # effet d'amas # erreur de moindre carrée # estimateur # estimation de rapport impair # estimation de vraissemblance maximum # exploration des grandes bases de données médicales # expérimentation toxicologique # génération de somme # modèle de probabilité de Poisson généralisée # méthode ...

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Probability and Statistics

Gene module detection methods aim to group genes with similar expression profiles to shed light into functional relationships and co-regulation, and infer gene regulatory networks. Methods proposed so far use clustering to group genes based on global similarity in their expression profiles (co-expression), bi-clustering to group genes and samples simultaneously, network inference to model regulatory relationships between genes. In this talk I will focus on multivariate matrix decomposition techniques that enable dimension reduction and the identification of molecular signatures.
We will consider two different types of assays: bulk and single cell assays. Bulk transcriptomics assays use RNA-sequencing techniques to monitor the average expression profile of all the constituent cells, but fail to identify the distinct transcriptional profiles from different cell types. Single cell assays use similar RNA-seq techniques (scRNA-seq) to those used for bulk cell populations, but provide unprecedented resolution at the cell level to understand cellular heterogeneity and uncover new biology. However, scRNA-seq present new computational and analytical challenges, because of their sheer size (100K - 500K of cells are sequenced) and their zero inflated distribution due to technical drop-outs.
I will illustrate how we can use matrix factorisation technique to mine these data and identify gene modules that underpin molecular mechanisms in cell identity in scRNA-seq. I will also give further perspective on how we could extend similar concepts to integrate different omics data types (e.g. bulk transcriptomics, proteomics, metabolomics) to identify tightly connected multi-omics signatures that holistically describe a biological system.
Gene module detection methods aim to group genes with similar expression profiles to shed light into functional relationships and co-regulation, and infer gene regulatory networks. Methods proposed so far use clustering to group genes based on global similarity in their expression profiles (co-expression), bi-clustering to group genes and samples simultaneously, network inference to model regulatory relationships between genes. In this talk I ...

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- xv; 552 p.
ISBN 978-1-4822-4819-7

Chapman & Hall / CRC biostatistics series

Localisation : Ouvrage RdC (BROE)

analyse de variance à mesure répétée # inférence bayésienne # biostatistiques # code WinBUGS # régression bayésienne # modèle linéaire

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- 431 p.
ISBN 978-3-540-33282-4

Mathématiques & applications , 0057

Localisation : Collection 1er étage

modèle aléatoire # biologie # biomathématique # chaine de Markov à temps discret # modèle discret # processus de Galton-Watson # ADN # modèle continu # chaine de Markov à temps continu # file d'attente # fiabilité

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- 476 p.
ISBN 978-0-387-95229-1

Statistics for biology and health

Localisation : Ouvrage RdC (EWEN)

bioinformatique # inférence statistique # biostatistique # inférence statistique # chaîne de Markov # modèle évoluant # ADN # séquence protéique # arbre phylogénétique

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- pag. mult.

Cahiers Scientifiques , 0007

Localisation : Ouvrage RdC (VOLT)

biologie mathématique # coexistence # individus # lutte pour la vie

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- 96 p.

Actualités Scientifiques et Industrielles , 0243

Localisation : Ouvrage RdC (VOLT)

association biologique # fluctuation # phénomène héréditaire

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- 39 p.

Actualités Scientifiques et industrielles , 0095

Localisation : Ouvrage RdC (TEIS)

biométrie # croissance # discontinuité # dysharmonie

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