Applied Biclustering Methods for Big and High Dimensional Data Using R

Applied Biclustering Methods for Big and High Dimensional Data Using R


  • Release: 2016-08-18
  • Publisher: CRC Press
  • Price: FREE
  • File: PDF, 433 page
  • ISBN: 9781315356396
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Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.

Biopharmaceutical Applied Statistics Symposium

Biopharmaceutical Applied Statistics Symposium


  • Release: 2018-09-03
  • Publisher: Springer
  • Price: FREE
  • File: PDF, 426 page
  • ISBN: 9789811078200
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This BASS book Series publishes selected high-quality papers reflecting recent advances in the design and biostatistical analysis of biopharmaceutical experiments – particularly biopharmaceutical clinical trials. The papers were selected from invited presentations at the Biopharmaceutical Applied Statistics Symposium (BASS), which was founded by the first Editor in 1994 and has since become the premier international conference in biopharmaceutical statistics. The primary aims of the BASS are: 1) to raise funding to support graduate students in biostatistics programs, and 2) to provide an opportunity for professionals engaged in pharmaceutical drug research and development to share insights into solving the problems they encounter. The BASS book series is initially divided into three volumes addressing: 1) Design of Clinical Trials; 2) Biostatistical Analysis of Clinical Trials; and 3) Pharmaceutical Applications. This book is the third of the 3-volume book series. The topics covered include: Targeted Learning of Optimal Individualized Treatment Rules under Cost Constraints, Uses of Mixture Normal Distribution in Genomics and Otherwise, Personalized Medicine – Design Considerations, Adaptive Biomarker Subpopulation and Tumor Type Selection in Phase III Oncology Trials, High Dimensional Data in Genomics; Synergy or Additivity - The Importance of Defining the Primary Endpoint, Full Bayesian Adaptive Dose Finding Using Toxicity Probability Interval (TPI), Alpha-recycling for the Analyses of Primary and Secondary Endpoints of Clinical Trials, Expanded Interpretations of Results of Carcinogenicity Studies of Pharmaceuticals, Randomized Clinical Trials for Orphan Drug Development, Mediation Modeling in Randomized Trials with Non-normal Outcome Variables, Statistical Considerations in Using Images in Clinical Trials, Interesting Applications over 30 Years of Consulting, Uncovering Fraud, Misconduct and Other Data Quality Issues in Clinical Trials, Development and Evalua

Clinical Trial Optimization Using R

Clinical Trial Optimization Using R


  • Release: 2017-08-10
  • Publisher: CRC Press
  • Price: FREE
  • File: PDF, 319 page
  • ISBN: 9781498735087
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Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making. This book presents in depth the Clinical Scenario Evaluation (CSE) framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs, multiplicity and subgroup analysis strategies, and overall development decision-making criteria around Go/No-Go. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well.

Market Segmentation Analysis

Market Segmentation Analysis


  • Release: 2018-07-20
  • Publisher: Springer
  • Price: FREE
  • File: PDF, 324 page
  • ISBN: 9789811088186
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This book is published open access under a CC BY 4.0 license. This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including an approach to exploring data structure and choosing a suitable number of market segments, and a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier. The book talks the reader through every single step, every single potential pitfall, and every single decision that needs to be made to ensure market segmentation analysis is conducted as well as possible. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.

Statistical Learning with Sparsity

Statistical Learning with Sparsity


  • Release: 2015-05-07
  • Publisher: CRC Press
  • Price: FREE
  • File: PDF, 367 page
  • ISBN: 9781498712170
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Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Co Clustering

Co Clustering


  • Release: 2013-12-11
  • Publisher: John Wiley & Sons
  • Price: FREE
  • File: PDF, 256 page
  • ISBN: 9781118649503
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Cluster or co-cluster analyses are important tools in a variety ofscientific areas. The introduction of this book presents a state ofthe art of already well-established, as well as more recent methodsof co-clustering. The authors mainly deal with the two-modepartitioning under different approaches, but pay particularattention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-basedclustering in particular. The authors briefly review the classicalclustering methods and focus on the mixture model. They present anddiscuss the use of different mixtures adapted to different types ofdata. The algorithms used are described and related works withdifferent classical methods are presented and commented upon. Thischapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted tothe latent block model proposed in the mixture approach context.The authors discuss this model in detail and present its interestregarding co-clustering. Various algorithms are presented in ageneral context. Chapter 3 focuses on binary and categorical data.It presents, in detail, the appropriated latent block mixturemodels. Variants of these models and algorithms are presented andillustrated using examples. Chapter 4 focuses on contingency data.Mutual information, phi-squared and model-based co-clustering arestudied. Models, algorithms and connections among differentapproaches are described and illustrated. Chapter 5 presents thecase of continuous data. In the same way, the different approachesused in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technologyof Compiègne, France. He is also a member of the CNRSLaboratory Heudiasyc (Heuristic and diagnostic of complex syst

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

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


  • Release: 2012-08-27
  • Publisher: Springer Science & Business Media
  • Price: FREE
  • File: PDF, 282 page
  • ISBN: 9783642240072
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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-friendly R packages that can be used to analyze this 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 inference under order restrictions and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include: • Multiplicity adjustment • Test statistics and procedures for the analysis of dose-response microarray data • Resampling-based inference and use of the SAM method for 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 • Gene set analysis to facilitate the interpretation of microarray results • Hierarchical Bayesian models and Bayesian variable selection • Non-linear models for dose-response microarray data • Multiple contrast tests • Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.

Data Clustering

Data Clustering


  • Release: 2018-09-03
  • Publisher: CRC Press
  • Price: FREE
  • File: PDF, 652 page
  • ISBN: 9781315360416
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Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Data Mining  Concepts and Techniques

Data Mining Concepts and Techniques


  • Release: 2011-06-09
  • Publisher: Elsevier
  • Price: FREE
  • File: PDF, 744 page
  • ISBN: 0123814804
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data