Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-f

Dose-finding experiments define the safe dosage of a drug in development, in terms of the quantity given to a patient. Statistical methods play a crucial role in identifying optimal dosage. Used appro

Discover the Latest Statistical Approaches for Modeling Exposure-Response RelationshipsWritten by an applied statistician with extensive practical experience in drug development,Exposure-Response Mode

This guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources. Concepts and

Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research c

Pharmacology in Drug Discovery and Development: Understanding Drug Response, Second Edition is an introductory resource illustrating how pharmacology can be used to furnish the tools necessary to anal

Batch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whi

In most modern biomedical research projects, application of high-throughput genomic, proteomic, and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray, next generation sequencing, mass spectrometry and proteomics assays. As the technologies have become mature and the price affordable, omics data are rapidly generated, and the problem of information integration and modeling of multi-lab and/or multi-omics data is becoming a growing one in the bioinformatics field. This book provides comprehensive coverage of these topics and will have a long-lasting impact on this evolving subject. Each chapter, written by a leader in the field, introduces state-of-the-art methods to handle information integration, experimental data, and database problems of omics data.

This book presents the theory and practice of major multilevel modeling techniques in a variety of contexts using R as the software tool. It describes the applications and extensions of multilevel mod

An accessible introduction to the use of regression analysis in the social sciencesRegression with Social Data: Modeling Continuous and Limited Response Variables represents the most complete and full

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for

Early Childhood Development in Tonga offers a comprehensive assessment of early child development across Tonga using the Tongan Early Human Capability Index instrument. The data has information on mor

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified f

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement

Modeling Atmospheric and Oceanic Flows: Insights from Laboratory Experiments and Numerical Simulations provides a broad overview of recent progress in using laboratory experiments and numerical simula

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.

“Data-Driven Modeling: Using MATLABR in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified f

Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the lin

Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming language to analyze spatial data arising from

The focus of early drug development has been the submission of an Investigational New Drug application to regulatory agencies. Early Drug Development: Strategies and Routes to First-in-Human Trials gu

While the utilization of microarrays for gene expression studies has been widely published, Microarray Methods for Drug Discovery describes the use of this technology for a multitude of other vital ap

A valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances In today's scientific research, there exists the need to address the topic of

In this new volume, renowned authors contribute fascinating, cutting-edge insights into microarray data analysis. Information on an array of topics is included in this innovative book including in-dep

This reference provides a concise overview of the key principles in dose selection and optimization and demonstrates applicability to recent successful new drug applications. Compiling key issues and

A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.Data scien

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical

Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemi

The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-

For readers who may not be statisticians or data analysts, Sarmento and Costa explain how to analyze data using the Python and R languages. They have particularly in mind, students and researchers

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement

This volume covers a large area, from the description of methodologies for data analysis to the real application. Chapters focus on methodologies for preprocessing of microarray data, a survey of miRN

The Oracle Press Guide to Big Data Analytics using RCowritten by members of the Big Data team at Oracle, this Oracle Press book focuses on analyzing data with R while making it scalable using Oracle’s

The only Oracle Press guide to MySQL Workbench explains how to design and model MySQL databases. MySQL Workbench Data Modeling and Development helps developers learn how to effectively use this power

This book presents a summary of the ways mass spectrometry is used in various stages of drug discovery and development including early support of medicinal chemistry, drug metabolism studies, distribu

A reference guide for applications of SEM using MplusStructural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results.Key features:Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus.Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data wi

As clinicians begin to realize the important role of dose-finding in the drug development process, there is an increasing openness to "novel" methods proposed in the past two decades. In particular, t