57.087.1 : Etude et traitement statistique de données biologiques

57.01 Fondements. Lois générales. Aspects théoriques. Caractéristiques et propriétés des être vivants.
57.01(031) Fondements. Lois générales. Aspects théoriques. Caractéristiques et propriétés des être vivants.(Encyclopédies)
57.017.6
57.088 Méthodes et techniques spéciales pour l'étude des molécules biologiques. Séparation. Centrifugation. Etude par les rayons X. Méthodes radio-isotopiques
57.097.1 Biométrie. Étude et traitement statistique de données biologique

Ouvrages de la bibliothèque en indexation 57.087.1

Statistical bioinformatics with R / Sunil Mathur 

Public ISBD Titre : Statistical bioinformatics with R Type de document : texte imprimé Auteurs : Sunil Mathur, Auteur Editeur : New York : Academic press Année de publication : 2010 Autre Editeur : Amsterdam : Elsevier Importance : XVI-319 p. Présentation : ill. Format : 25 cm ISBN/ISSN/EAN : 978-0-12-375104-1 Note générale : Bibliogr. p. 305-314. Index Langues : Anglais (eng) Mots-clés : Bioinformatique  --  Méthodes  statistiques R  (logiciel) Bioinformatics  --  Statistical  methods R  (Computer  program  language) Index. décimale : 57.087.1 Etude et traitement statistique de données biologiques Résumé : Designed for a one or two semester bioinformatics course at the senior undergraduate or graduate level, this book takes a broad view of bioinformatics—not just gene expression and not just sequence analysis. A careful balance of statistical theory in the context of bioinformatics applications, including the development of advanced methodology such as Bayesian and Markov models provides students with the underlying foundation needed to conduct bioinformatics. A wide variety of applications in different biomedical and genomic areas, including the identification of differentially expressed genes, sequence analysis, location of recombinant breakpoints, complex designs, and gene clustering, are included. Statisticians interested in bioinformatics and applied science researchers interested in finding solutions to high-dimensional problems in their fields will find this an essential reference. The inclusion of R code is unique and a real advantage to graduate students and beginning researchers. Prerequisite knowledge includes one semester of calculus and an introduction to statistics course Note de contenu : 1.Introduction 2.Microarrays 3.Probability and Statistical Theory 4.Special Distributions Properties and Applications 5.Statistical Inference and Applications 6.Nonparametric Statistics 7.Bayesian Statistics 8.Markov Chain Monte Carlo 9.Analysis of Variance 10.The Design of Experiments 11.Multiple Testing of Hypotheses En ligne : http://books.google.dz/books/about/Statistical_Bioinformatics.html?id=wbJo7L_8CQ [...] Statistical bioinformatics with R [texte imprimé] / Sunil Mathur, Auteur . - New York : Academic press : Amsterdam : Elsevier, 2010 . - XVI-319 p. : ill. ; 25 cm. ISBN : 978-0-12-375104-1 Bibliogr. p. 305-314. Index Langues : Anglais (eng) Mots-clés : Bioinformatique  --  Méthodes  statistiques R  (logiciel) Bioinformatics  --  Statistical  methods R  (Computer  program  language) Index. décimale : 57.087.1 Etude et traitement statistique de données biologiques Résumé : Designed for a one or two semester bioinformatics course at the senior undergraduate or graduate level, this book takes a broad view of bioinformatics—not just gene expression and not just sequence analysis. A careful balance of statistical theory in the context of bioinformatics applications, including the development of advanced methodology such as Bayesian and Markov models provides students with the underlying foundation needed to conduct bioinformatics. A wide variety of applications in different biomedical and genomic areas, including the identification of differentially expressed genes, sequence analysis, location of recombinant breakpoints, complex designs, and gene clustering, are included. Statisticians interested in bioinformatics and applied science researchers interested in finding solutions to high-dimensional problems in their fields will find this an essential reference. The inclusion of R code is unique and a real advantage to graduate students and beginning researchers. Prerequisite knowledge includes one semester of calculus and an introduction to statistics course Note de contenu : 1.Introduction 2.Microarrays 3.Probability and Statistical Theory 4.Special Distributions Properties and Applications 5.Statistical Inference and Applications 6.Nonparametric Statistics 7.Bayesian Statistics 8.Markov Chain Monte Carlo 9.Analysis of Variance 10.The Design of Experiments 11.Multiple Testing of Hypotheses En ligne : http://books.google.dz/books/about/Statistical_Bioinformatics.html?id=wbJo7L_8CQ [...]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
052630	57.087.1 MAT	Papier	Bibliothèque Centrale	Biologie	Disponible	En bon état