Información del autor
Autor Shkedy, Ziv |
Documentos disponibles escritos por este autor (2)
Refinar búsquedaModeling Dose-Response Microarray Data in Early Drug Development Experiments Using R / SpringerLink (Online service) ; Lin, Dan ; Shkedy, Ziv ; Yekutieli, Daniel ; Dhammika Amaratunga ; Luc Bijnens (2012)
Título : Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order-Restricted Analysis of Microarray Data Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Lin, Dan ; Shkedy, Ziv ; Yekutieli, Daniel ; Dhammika Amaratunga ; Luc Bijnens Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: Use R! Número de páginas: XV, 282 p. 96 illus., 4 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-642-24007-2 Idioma : Inglés (eng) Palabras clave: Statistics Pharmaceutical technology Bioinformatics Biostatistics Computational biology Statistics, general and Computing/Statistics Programs Sciences/Technology Computer Appl. in Life Sciences Clasificación: 51 Matemáticas Resumen: This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as the likelihood ratio test and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book. Methodological topics discussed include: · Multiplicity adjustment · Test statistics and testing procedures for the analysis of dose-response microarray data · Resampling-based inference and use of the SAM method at the presence of small-variance genes in the data · Identification and classification of dose-response curve shapes · Clustering of order restricted (but not necessarily monotone) dose-response profiles · Hierarchical Bayesian models and non-linear models for dose-response microarray data · Multiple contrast tests All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments Nota de contenido: Introduction -- Part I: Dose-response Modeling: An Introduction -- Estimation Under Order Restrictions -- The Likelihood Ratio Test -- Part II: Dose-response Microarray Experiments -- Functional Genomic Dose-response Experiments -- Adjustment for Multiplicity -- Test for Trend -- Order Restricted Bisclusters -- Classification of Trends in Dose-response Microarray Experiments Using Information Theory Selection Methods -- Multiple Contrast Test -- Confidence Intervals for the Selected Parameters -- Case Study Using GUI in R: Gene Expression Analysis After Acute Treatment With Antipsychotics En línea: http://dx.doi.org/10.1007/978-3-642-24007-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32941 Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order-Restricted Analysis of Microarray Data [documento electrónico] / SpringerLink (Online service) ; Lin, Dan ; Shkedy, Ziv ; Yekutieli, Daniel ; Dhammika Amaratunga ; Luc Bijnens . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012 . - XV, 282 p. 96 illus., 4 illus. in color : online resource. - (Use R!) .
ISBN : 978-3-642-24007-2
Idioma : Inglés (eng)
Palabras clave: Statistics Pharmaceutical technology Bioinformatics Biostatistics Computational biology Statistics, general and Computing/Statistics Programs Sciences/Technology Computer Appl. in Life Sciences Clasificación: 51 Matemáticas Resumen: This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as the likelihood ratio test and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book. Methodological topics discussed include: · Multiplicity adjustment · Test statistics and testing procedures for the analysis of dose-response microarray data · Resampling-based inference and use of the SAM method at the presence of small-variance genes in the data · Identification and classification of dose-response curve shapes · Clustering of order restricted (but not necessarily monotone) dose-response profiles · Hierarchical Bayesian models and non-linear models for dose-response microarray data · Multiple contrast tests All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments Nota de contenido: Introduction -- Part I: Dose-response Modeling: An Introduction -- Estimation Under Order Restrictions -- The Likelihood Ratio Test -- Part II: Dose-response Microarray Experiments -- Functional Genomic Dose-response Experiments -- Adjustment for Multiplicity -- Test for Trend -- Order Restricted Bisclusters -- Classification of Trends in Dose-response Microarray Experiments Using Information Theory Selection Methods -- Multiple Contrast Test -- Confidence Intervals for the Selected Parameters -- Case Study Using GUI in R: Gene Expression Analysis After Acute Treatment With Antipsychotics En línea: http://dx.doi.org/10.1007/978-3-642-24007-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32941 Ejemplares
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Título : Modeling Infectious Disease Parameters Based on Serological and Social Contact Data : A Modern Statistical Perspective Tipo de documento: documento electrónico Autores: Hens, Niel ; SpringerLink (Online service) ; Shkedy, Ziv ; Marc Aerts ; Faes, Christel ; Van Damme, Pierre ; Philippe Beutels Editorial: New York, NY : Springer New York Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: Statistics for Biology and Health, ISSN 1431-8776 num. 63 Número de páginas: XVI, 300 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-4072-7 Idioma : Inglés (eng) Palabras clave: Statistics for Life Sciences, Medicine, Health Sciences Statistics, general Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: Mathematical epidemiology of infectious diseases usually involves describing the flow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, often referred to as the force of infection. The force of infection reflects the degree of contact with potential for transmission between infected and susceptible individuals. The mathematical relation between the force of infection and effective contact patterns is generally assumed to be subjected to the mass action principle, which yields the necessary information to estimate the basic reproduction number, another key parameter in infectious disease epidemiology. It is within this context that the Center for Statistics (CenStat, I-Biostat, Hasselt University) and the Centre for the Evaluation of Vaccination and the Centre for Health Economic Research and Modelling Infectious Diseases (CEV, CHERMID, Vaccine and Infectious Disease Institute, University of Antwerp) have collaborated over the past 15 years. This book demonstrates the past and current research activities of these institutes and can be considered to be a milestone in this collaboration. This book is focused on the application of modern statistical methods and models to estimate infectious disease parameters. We want to provide the readers with software guidance, such as R packages, and with data, as far as they can be made publicly available. Nota de contenido: Mathematical models for infectious diesease -- The static model -- The dynamic model -- The stochastic model -- Implementation of models in MATLAB -- Data sources for modelling infectious diseases -- Estimation from serological data -- Parametric models for teh prevalence and the force of infection -- Non-parametric approaches to model the prevalence and force of infection -- Semi-parametric approaches to model the prevalence and force of infection -- A Bayesian approach -- Modelling the prevalence and the force of infection direction from antibody levels -- Modelling multivariate serological data -- Estimation from other data sources -- Estimating mixing patterns and Ro in a heterogenous population -- Modelling in a homogeneous population -- Modelling in a heterogeneous population -- Modelling AIDS outbreak data -- Modelling hepatitis C among injection drug users -- Modelling dengue -- Modelling bovine herpes virus in cattle En línea: http://dx.doi.org/10.1007/978-1-4614-4072-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32833 Modeling Infectious Disease Parameters Based on Serological and Social Contact Data : A Modern Statistical Perspective [documento electrónico] / Hens, Niel ; SpringerLink (Online service) ; Shkedy, Ziv ; Marc Aerts ; Faes, Christel ; Van Damme, Pierre ; Philippe Beutels . - New York, NY : Springer New York : Imprint: Springer, 2012 . - XVI, 300 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776; 63) .
ISBN : 978-1-4614-4072-7
Idioma : Inglés (eng)
Palabras clave: Statistics for Life Sciences, Medicine, Health Sciences Statistics, general Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: Mathematical epidemiology of infectious diseases usually involves describing the flow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, often referred to as the force of infection. The force of infection reflects the degree of contact with potential for transmission between infected and susceptible individuals. The mathematical relation between the force of infection and effective contact patterns is generally assumed to be subjected to the mass action principle, which yields the necessary information to estimate the basic reproduction number, another key parameter in infectious disease epidemiology. It is within this context that the Center for Statistics (CenStat, I-Biostat, Hasselt University) and the Centre for the Evaluation of Vaccination and the Centre for Health Economic Research and Modelling Infectious Diseases (CEV, CHERMID, Vaccine and Infectious Disease Institute, University of Antwerp) have collaborated over the past 15 years. This book demonstrates the past and current research activities of these institutes and can be considered to be a milestone in this collaboration. This book is focused on the application of modern statistical methods and models to estimate infectious disease parameters. We want to provide the readers with software guidance, such as R packages, and with data, as far as they can be made publicly available. Nota de contenido: Mathematical models for infectious diesease -- The static model -- The dynamic model -- The stochastic model -- Implementation of models in MATLAB -- Data sources for modelling infectious diseases -- Estimation from serological data -- Parametric models for teh prevalence and the force of infection -- Non-parametric approaches to model the prevalence and force of infection -- Semi-parametric approaches to model the prevalence and force of infection -- A Bayesian approach -- Modelling the prevalence and the force of infection direction from antibody levels -- Modelling multivariate serological data -- Estimation from other data sources -- Estimating mixing patterns and Ro in a heterogenous population -- Modelling in a homogeneous population -- Modelling in a heterogeneous population -- Modelling AIDS outbreak data -- Modelling hepatitis C among injection drug users -- Modelling dengue -- Modelling bovine herpes virus in cattle En línea: http://dx.doi.org/10.1007/978-1-4614-4072-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32833 Ejemplares
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