Bøger i Applied Bioinformatics and Biostatistics in Cancer Research serien

Molecular biology has rapidly advanced since the discovery of the basic flow of information in life, from DNA to RNA to proteins. While there are several important and interesting exceptions to this general flow of information, the importance of these biological macromolecules in dictating the phenotypic nature of living creatures in health and disease is paramount. In the last one and a half decades, and particularly after the completion of the Human Genome Project, there has been an explosion of technologies that allow the broad characterization of these macromolecules in physiology, and the perturbations to these macromolecules that occur in diseases such as cancer. In this volume, we will explore the modern approaches used to characterize these macromolecules in an unbiased, systematic way. Such technologies are rapidly advancing our knowledge of the coordinated and complicated changes that occur during carcinogenesis, and are providing vital information that, when correctly interpreted by biostatistical/bioinformatics analyses, can be exploited for the prevention, diagnosis, and treatment of human cancers.The purpose of this volume is to provide an overview of modern molecular biological approaches to unbiased discovery in cancer research. Advances in molecular biology allowing unbiased analysis of changes in cancer initiation and progression will be overviewed. These include the strategies employed in modern genomics, gene expression analysis, and proteomics.

This volume covers classic as well as cutting-edge topics on the analysis of clinical trial data in biomedical and psychosocial research and discusses each topic in an expository and user-friendly fashion.

This book will examine current issues and controversies in the design of clinical trials, including topics in adaptive and sequential designs, the design of correlative genomic studies, the design of studies in which missing data is anticipated.

This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.

This book will examine current issues and controversies in the design of clinical trials, including topics in adaptive and sequential designs, the design of correlative genomic studies, the design of studies in which missing data is anticipated.

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome.

This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome.

This volume covers classic as well as cutting-edge topics on the analysis of clinical trial data in biomedical and psychosocial research and discusses each topic in an expository and user-friendly fashion.