Principles of Statistical Genomics

Analysis in Nutrition Research: Principles of Statistical Methodology and Interpretation of the Results describes, in a comprehensive manner, the methodologies of quantitative analysis of data origina

Untangling Molecular Biodiversity presents a unique global framework to explain molecular and organismal biodiversity that is grounded in evolutionary genomics. This book will tackle important questio

This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after hav

"Applied Computational Genomics" focuses on an in-depth review of statistical development and application in the area of human genomics including candidate gene mapping, linkage analysis, population-b

Advances in chemistry, biology and genomics coupled with laboratory automation and computational technologies have led to the rapid emergence of the multidisciplinary field of chemical genomics. This edited text, with contributions from experts in the field, discusses the new techniques and applications that help further the study of chemical genomics. The beginning chapters provide an overview of the basic principles of chemical biology and chemical genomics. This is followed by a technical section that describes the sources of small-molecule chemicals; the basics of high-throughput screening technologies; and various bioassays for biochemical-, cellular- and organism-based screens. The final chapters connect the chemical genomics field with personalized medicine and the druggable genome for future discovery of new therapeutics. This book will be valuable to researchers, professionals and graduate students in many fields, including biology, biomedicine and chemistry.

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. Th

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. Th

This graduate-level text provides a survey of the logic and reasoning underpinning statistical analysis, as well as giving a broad-brush overview of the various statistical techniques that play a majo

Microbial Functional Genomics offers a timely summary of the principles, approaches, and applications. It presents a comprehensive review of microbial functional genomics, covering microbial diversity

Human Population Genetics and Genomics provides researchers/students with knowledge on population genetics and relevant statistical approaches to help them become more effective users of modern geneti

One of Ian Hacking's earliest publications, this book showcases his early ideas on the central concepts and questions surrounding statistical reasoning. He explores the basic principles of statistical reasoning and tests them, both at a philosophical level and in terms of their practical consequences for statisticians. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Jan-Willem Romeijn, illuminating its enduring importance and relevance to philosophical enquiry, Hacking's influential and original work has been revived for a new generation of readers.

One of Ian Hacking's earliest publications, this book showcases his early ideas on the central concepts and questions surrounding statistical reasoning. He explores the basic principles of statistical reasoning and tests them, both at a philosophical level and in terms of their practical consequences for statisticians. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Jan-Willem Romeijn, illuminating its enduring importance and relevance to philosophical enquiry, Hacking's influential and original work has been revived for a new generation of readers.

Econometrics is a branch of economics that aims to analyze financial and economic data sets by using various statistical, mathematical and computational tools. The aim of this book is to present resea

This book presents a much-needed synthesis of recent research on copulas, a statistical tool widely used in applications in financial risk assessment and actuarial analysis. The text provides an overv

This fourth edition of the best-selling textbook, Human Genetics and Genomics, clearly explains the key principles needed by medical and health sciences students, from the basis of molecular genetics,

Applied statistics is more than data analysis, but it is easy to lose sight of the big picture. David Cox and Christl Donnelly distil decades of scientific experience into usable principles for the successful application of statistics, showing how good statistical strategy shapes every stage of an investigation. As you advance from research or policy question, to study design, through modelling and interpretation, and finally to meaningful conclusions, this book will be a valuable guide. Over a hundred illustrations from a wide variety of real applications make the conceptual points concrete, illuminating your path and deepening your understanding. This book is essential reading for anyone who makes extensive use of statistical methods in their work.

Applied statistics is more than data analysis, but it is easy to lose sight of the big picture. David Cox and Christl Donnelly distil decades of scientific experience into usable principles for the successful application of statistics, showing how good statistical strategy shapes every stage of an investigation. As you advance from research or policy question, to study design, through modelling and interpretation, and finally to meaningful conclusions, this book will be a valuable guide. Over a hundred illustrations from a wide variety of real applications make the conceptual points concrete, illuminating your path and deepening your understanding. This book is essential reading for anyone who makes extensive use of statistical methods in their work.

Based on the authors’ lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it co

Josiah Willard Gibbs (1839–1903) was the greatest American mathematician and physicist of the nineteenth century. He played a key role in the development of vector analysis (his book on this topic is also reissued in this series), but his deepest work was in the development of thermodynamics and statistical physics. This book, Elementary Principles in Statistical Mechanics, first published in 1902, gives his mature vision of these subjects. Mathematicians, physicists and engineers familiar with such things as Gibbs entropy, Gibbs inequality and the Gibbs distribution will find them here discussed in Gibbs' own words.

Cells are complex objects, representing a multitude of structures and processes. In order to understand the organization, interaction and hierarchy of these structures and processes, a quantitative understanding is absolutely critical. Traditionally, statistical mechanics-based treatment of biological systems has focused on the molecular level, with larger systems being ignored. This book integrates understanding from the molecular to the cellular and multi-cellular level in a quantitative framework that will benefit a wide audience engaged in biological, biochemical, biophysical and clinical research. It will build new bridges of quantitative understanding that link fundamental physical principles governing cellular structure and function with implications in clinical and biomedical contexts.

This rigorous and practical account of the interpretation of mutagenicity test data draws upon the expertise of toxicologists and statisticians. Chemicals, such as drugs, food additives and pesticides, all need careful screening to eliminate potentially mutagenic compounds. Although guidelines exist on the performance of these tests, advice on data evaluation is scarce; this volume provides the statistical background necessary for toxicologists to understand, design and interpret mutagenicity tests. In addition to the nine chapters dealing with the different tests employed, there is an introductory chapter on some of the statistical principles involved, a glossary of useful terms and an appendix providing vital information on the availability of computer software. A large team of contributors and editors working under the auspices of the United Kingdom Environmental Mutagen Society (UKEMS) have contributed a wealth of first-hand experience in compiling this immensely practical book of

A key problem in practical image processing is that of detecting certain features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. A number of computationally efficient algorithms and techniques are then presented, to deal with such problems as line, edge and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

The fission yeast Schizosaccharomyces pombe is the favoured tool of many productive research groups throughout the world, serving as a useful model for fundamental principles and mechanisms, such as g

This graduate textbook explores the atomistic aspects of the mechanical behavior of solids. Weiner (Brown University) applies the principles of classical statistical mechanics to the study of the the

A textbook for biomedical students that encourages them to think about data and statistical procedures rather than merely to learn the simplest and quickest techniques. It describes many new methods a

A key problem in practical image processing is that of detecting certain features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. A number of computationally efficient algorithms and techniques are then presented, to deal with such problems as line, edge and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

This rigorous and practical account of the interpretation of mutagenicity test data draws upon the expertise of toxicologists and statisticians. Chemicals, such as drugs, food additives and pesticides, all need careful screening to eliminate potentially mutagenic compounds. Although guidelines exist on the performance of these tests, advice on data evaluation is scarce; this volume provides the statistical background necessary for toxicologists to understand, design and interpret mutagenicity tests. In addition to the nine chapters dealing with the different tests employed, there is an introductory chapter on some of the statistical principles involved, a glossary of useful terms and an appendix providing vital information on the availability of computer software. A large team of contributors and editors working under the auspices of the United Kingdom Environmental Mutagen Society (UKEMS) have contributed a wealth of first-hand experience in compiling this immensely practical book of

This classic text combines thermodynamics, statistical mechanics, and kinetic theory in a single unified presentation of thermal physics. The three-part treatment covers the principles of statistical

A lucid presentation of statistical physics and thermodynamics which develops from the general principles to give a large number of applications of the theory.

This is a straightforward and practical introduction to statistics for students without any advanced knowledge of mathematics who need to use statistical techniques. The author provides a wide selection of effective tools of the trade so that the reader can tackle a whole variety of concrete situations. No attempt is made to give a comprehensive account of the subject. Enough technique is explained so that the reader can appreciate when there is a need to turn to more advanced material elsewhere. The basic mathematics required is at the level of senior secondary school education, although even less is necessary for the earlier chapters of the book.

This book is a philosophical study of the basic principles of statistical reasoning. Professor Hacking has sought to discover the simple principles which underlie modern work in mathematical statistics and to test them, both at a philosophical level and in terms of their practical consequences fort statisticians. The ideas of modern logic are used to analyse these principles, and results are presented without the use of unfamiliar symbolism. It begins with a philosophical analysis of a few central concepts and then, using an elementary system of logic, develops most of the standard statistical theory. the analysis provides answers to many disputed questions about how to test statistical hypotheses and about how to estimate quantities in the light of statistical data. One product of the analysis is a sound and consistent rationale for R. A. Fisher's controversial concept of 'fiducial probability'.

Best Practices in Genetic and Genomic Research: Rigor, Reproducibility, and Protocols for the Laboratory and Classroom provides a full methodological and statistical overview for researchers, clinicians, students, and post-doctoral fellows conducting genetic and genomic research. Here, active geneticists, clinicians, and bioinformaticists offer practical solutions for a variety of challenges associated with several modern approaches in genetics and genomics, including genotyping, gene expression analysis, epigenetic analysis, GWAS, EWAS, genomic sequencing, and gene editing. Emphasis is placed on rigor and reproducibility throughout, with each section containing laboratory case-studies and classroom activities covering step-by-step protocols, best practices, and common pitfalls. Specific genetic and genomic technologies discussed include microarray analysis, DNA-seq, RNA-seq, Chip-Seq, methyl-seq, CRISPR gene editing, and CRISPR-based genetic analysis. Training exercises, supporting da

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies t

This book is a mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation, and an invaluable reference for researchers and professionals. With a wealth of illustrations and examples to explain the key features of the theory and to connect with real-world applications, additional material to explore more advanced concepts, and numerous end-of-chapter problems to test the reader's knowledge, this textbook is the 'go-to' guide for learning about the core principles of statistical inference and its application in engineering and data science. The password-protected solutions manual and the image gallery from the book are available online.

The branch of physics which uses three basic laws namely electromagnetism, statistical mechanics and quantum mechanics, to understand the physical and behavioral properties of condensed matter is know

The purpose of this book is to present results on the subject of weak convergence in function spaces to study invariance principles in statistical applications to dependent random variables, U-statist