Applied Bioinformatics and Biostatistics in Cancer Research – serie

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.

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. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

Computational biology is an interdisciplinary research that applies approaches and methodologies of information sciences and engineering to address complex pr- lems in biology. With rapid developments in the omics and computer technologies over the past decade, computational biology has been evolving to cover a much wider research domain and applications in order to adequately address challenging problems in systems biology and medicine. This edited book focuses on recent - sues and applications of computational biology in oncology. This book contains 11 chapters that cover diverse advanced computationalmethods applied to oncologyin an attempt to ?nd more effective ways for the diagnosis and cure of cancer. Chapter 1 by Chen and Nguyen addresses an analysis of cancer genomics data using partial least squares weights for identifying relevant genes, which are useful for follow-up validations. In Chap. 2, Zhao and Yan report an interesting biclust- ing method for microarray data analysis, which can handle the case when only a subset of genes coregulates under a subset of conditions and appears to be a novel technique for classifying cancer tissues.As another computational method for - croarray data analysis, the work by Le Cao and McLachlan in Chap. 3 discusses the dif?culties encountered when dealing with microarray data subjected to sel- tion bias, multiclass, and unbalanced problems, which can be overcome by careful selection of gene expression pro?les. Novel methods presented in these chapters can be applied for developing diagnostic tests and therapeutic treatments for cancer patients.

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. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

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.  Each chapter will be written by an expert conducting research in the topic of that chapter.  As a collection, the chapters would be intended to serve as a guidance for statisticians designing trials.

Computational biology is an interdisciplinary research that applies approaches and methodologies of information sciences and engineering to address complex pr- lems in biology. With rapid developments in the omics and computer technologies over the past decade, computational biology has been evolving to cover a much wider research domain and applications in order to adequately address challenging problems in systems biology and medicine. This edited book focuses on recent - sues and applications of computational biology in oncology. This book contains 11 chapters that cover diverse advanced computationalmethods applied to oncologyin an attempt to ?nd more effective ways for the diagnosis and cure of cancer. Chapter 1 by Chen and Nguyen addresses an analysis of cancer genomics data using partial least squares weights for identifying relevant genes, which are useful for follow-up validations. In Chap. 2, Zhao and Yan report an interesting biclust- ing method for microarray data analysis, which can handle the case when only a subset of genes coregulates under a subset of conditions and appears to be a novel technique for classifying cancer tissues.As another computational method for - croarray data analysis, the work by Le Cao and McLachlan in Chap. 3 discusses the dif?culties encountered when dealing with microarray data subjected to sel- tion bias, multiclass, and unbalanced problems, which can be overcome by careful selection of gene expression pro?les. Novel methods presented in these chapters can be applied for developing diagnostic tests and therapeutic treatments for cancer patients.

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 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.  Each chapter will be written by an expert conducting research in the topic of that chapter.  As a collection, the chapters would be intended to serve as a guidance for statisticians designing trials.

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. The intent of the book is to provide an overview of the primary statistical and data analytic issues associated with each of the selected topics, followed by a discussion of approaches for tackling such issues and available software packages for carrying out analyses. While classic topics such as survival data analysis, analysis of diagnostic test data and assessment of measurement reliability are well known and covered in depth by available topic-specific texts, this volume serves a different purpose: it provides a quick introduction to each topic for self-learning, particularly for those who have not done any formal coursework on a given topic but must learn it due to its relevance to their multidisciplinary research. In addition, the chapters on these classic topics will reflect issues particularly relevant to modern clinical trials such as longitudinal designs and new methods for analyzing data from such study designs. The coverage of these topics provides a quick introduction to these important statistical issues and methods for addressing them. As with the classic topics, this part of the volume on modern topics will enable researchers to grasp the statistical methods for addressing these emerging issues underlying modern clinical trials and to apply them to their research studies.