Applied Statistical Genetics with R

Applied Statistical Genetics with R PDF Author: Andrea S. Foulkes
Publisher: Springer Science & Business Media
ISBN: 038789554X
Category : Science
Languages : en
Pages : 264

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Book Description
Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

Applied Statistical Genetics with R

Applied Statistical Genetics with R PDF Author: Andrea S. Foulkes
Publisher: Springer Science & Business Media
ISBN: 038789554X
Category : Science
Languages : en
Pages : 264

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Book Description
Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

An Introduction to Genetic Epidemiology

An Introduction to Genetic Epidemiology PDF Author: Palmer, Lyle J.
Publisher: Policy Press
ISBN: 1861348975
Category : Medical
Languages : en
Pages : 240

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Book Description
This book brings together leading experts to provide an introduction to genetic epidemiology that begins with a primer in human molecular genetics through all the standard methods in population genetics and genetic epidemiology required for an adequate grounding in the field.

Essential Statistical Methods for Medical Statistics

Essential Statistical Methods for Medical Statistics PDF Author: J. Philip Miller
Publisher: Elsevier
ISBN: 0444537384
Category : Mathematics
Languages : en
Pages : 363

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Book Description
Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas. - Contributors are internationally renowned experts in their respective areas - Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research - Methods for assessing Biomarkers, analysis of competing risks - Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs - Structural equations modelling and longitudinal data analysis

Computational Intelligence Methods for Bioinformatics and Biostatistics

Computational Intelligence Methods for Bioinformatics and Biostatistics PDF Author: Enrico Formenti
Publisher: Springer
ISBN: 3319090429
Category : Computers
Languages : en
Pages : 281

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Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2013, held in Nice, France in June 2013. The 19 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections on bioinformatics, biostatistics, knowledge based medicine, and data integration and analysis in omic-science.

Using 21st Century Science to Improve Risk-Related Evaluations

Using 21st Century Science to Improve Risk-Related Evaluations PDF Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309453488
Category : Nature
Languages : en
Pages : 201

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Book Description
Over the last decade, several large-scale United States and international programs have been initiated to incorporate advances in molecular and cellular biology, -omics technologies, analytical methods, bioinformatics, and computational tools and methods into the field of toxicology. Similar efforts are being pursued in the field of exposure science with the goals of obtaining more accurate and complete exposure data on individuals and populations for thousands of chemicals over the lifespan; predicting exposures from use data and chemical-property information; and translating exposures between test systems and humans. Using 21st Century Science to Improve Risk-Related Evaluations makes recommendations for integrating new scientific approaches into risk-based evaluations. This study considers the scientific advances that have occurred following the publication of the NRC reports Toxicity Testing in the 21st Century: A Vision and a Strategy and Exposure Science in the 21st Century: A Vision and a Strategy. Given the various ongoing lines of investigation and new data streams that have emerged, this publication proposes how best to integrate and use the emerging results in evaluating chemical risk. Using 21st Century Science to Improve Risk-Related Evaluations considers whether a new paradigm is needed for data validation, how to integrate the divergent data streams, how uncertainty might need to be characterized, and how best to communicate the new approaches so that they are understandable to various stakeholders.

Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology

Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology PDF Author: Arora, Geeta
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 356

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Book Description
In today's data-driven world, understanding and interpreting statistical information is more critical than ever, especially in medicine, where statistical methods are used to design and analyze clinical trials, study the distribution of disease in populations, and develop new treatments. In the era of evidence-based medicine, Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology addresses the critical need for a grasp of statistical concepts. This book delves into biostatistics, clinical trials, and epidemiology, offering a robust foundation for understanding and interpreting statistical information in medicine. It explores biostatistics, elucidating fundamental elements such as probability, sampling, and hypothesis testing. The section on clinical trials covers the entire spectrum from trial design to ethical considerations, providing an invaluable resource for researchers navigating the complexities of medical research. Epidemiology, a cornerstone of public health, is examined in the book, offering insights into the distribution and determinants of diseases in populations. The application-focused section further extends the utility of medical statistics, encompassing public health, healthcare policy, and drug development.

Graduate Programs in the Biological Sciences 2008

Graduate Programs in the Biological Sciences 2008 PDF Author: Peterson's Guides Staff
Publisher: Peterson Nelnet Company
ISBN: 9780768924060
Category : Education
Languages : en
Pages : 1642

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Book Description
The six volumes of Peterson's Annual Guides to Graduate Study, the only annually updated reference work of its kind, provide wide-ranging information on the graduate and professional programs offered by accredited colleges and universities in the United States and U.S. territories and those in Canada, Mexico, Europe, and Africa that are accredited by U.S. accrediting bodies. Books 2 through 6 are divided into sections that contain one or more directories devoted to individual programs in a particular field. Book 3 contains more than 4,000 programs of study in 53 disciplines of the biological sciences.

Biostatistics for Human Genetic Epidemiology

Biostatistics for Human Genetic Epidemiology PDF Author: Bertram K. C. Chan
Publisher: Springer
ISBN: 331993791X
Category : Medical
Languages : en
Pages : 368

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Book Description
The book illustrates how biostatistics may numerically summarize human genetic epidemiology using R, and may be used successfully to solve problems in quantitative Genetic Epidemiology Biostatistics for Human Genetic Epidemiology provides statistical methodologies and R recipes for human genetic epidemiologic problems. It begins by introducing all the necessary probabilistic and statistical foundations, before moving on to topics related human genetic epidemiology, with R codes illustrations for various examples. This clear and concise book covers human genetic epidemiology, using R in data analysis, including multivariate data analysis. It examines probabilistic and statistical theories for modeling human genetic epidemiology – leading the readers through an effective epidemiologic model, from simple to advanced levels. Classical mathematical, probabilistic, and statistical theory are thoroughly discussed and presented. This book also presents R as a calculator and using R in data analysis. Additionally, it covers Advanced Human Genetic Data Concepts, the Study of Human Genetic Variation, Manhattan Plots, as well as the Procedures for Multiple Comparison. Numerous Worked Examples are provided for illustrations of concepts and real-life applications. Biostatistics for Human Genetic Epidemiology is an ideal reference for professionals and students in Medicine (particularly in Preventive Medicine and Public Health Medical Practices), as well as in Genetics, Epidemiology, and Biostatistics.

High-Dimensional Data Analysis in Cancer Research

High-Dimensional Data Analysis in Cancer Research PDF Author: Xiaochun Li
Publisher: Springer Science & Business Media
ISBN: 0387697659
Category : Medical
Languages : en
Pages : 164

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Book Description
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.

Application of Novel Statistical and Machine-learning Methods to High-dimensional Clinical Cancer and (Multi-)Omics data

Application of Novel Statistical and Machine-learning Methods to High-dimensional Clinical Cancer and (Multi-)Omics data PDF Author: Chao Xu
Publisher: Frontiers Media SA
ISBN: 2889714365
Category : Science
Languages : en
Pages : 136

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Book Description