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Modern Statistics for Modern Biology de Susan Holmes,Wolfgang Huber
Descripción - Críticas 'This is a gorgeous book, both visually and intellectually, superbly suited for anyone who wants to learn the nuts and bolts of modern computational biology. It can also be a practical, hands-on starting point for life scientists and students who want to break out of 'canned packages' into the more versatile world of R coding. Much richer than the typical statistics textbook, it covers a wide range of topics in machine learning and image processing. The chapter on making high-quality graphics is alone worth the price of the book.' William H. Press, University of Texas, Austin'The book is a timely, comprehensive and practical reference for anyone working with modern quantitative biotechnologies. It can be read at multiple levels. For scientists with a statistics background, it is a thorough review of key methods for design and analysis of high-throughput experiments. For life scientists with a limited exposure to statistics, it offers a series of examples with relevant data and R code. Avoiding buzzwords and hype, the book advocates appropriate statistical practice for reproducible research. I expect it to be as influential for the life sciences community as Modern Applied Statistics with S, by Venables and Ripley or Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani are for applied statistics.' Olga Vitek, Northeastern University, Boston'Navigating rich data to arrive at sensible insight requires confidence in our biological understanding, informatic ability, statistical sophistication, and skills at effective communication. Fortunately the wisdom and effort of the worldwide research community has been distilled into accessible and rich collections of R and Bioconductor software packages. Holmes and Huber provide a comprehensive guide to navigating modern statistical methods for working with complex, large, and nuanced biological data. The presentation provides a firm conceptual foundation coupled with worked practical examples, extended analysis, and refined discussion of practical and theoretical challenges facing the modern practitioner. This book provides us with the confidence and tools necessary for the analysis and comprehension of modern biological data using modern statistical methods.' Martin Morgan, Roswell Park Comprehensive Cancer Center, leader of the Bioconductor project'Holmes and Huber take an integrated approach to presenting the key statistical concepts and methods needed for the analysis of biological data. Specifically, they do a wonderful job of building these foundations in the context of modern computational tools, genuine scientific questions, and real-world datasets. The code showcases many of the newest features of R and its dynamic package ecosystem, such as using ggplot2 for visualization and dplyr for data manipulation.' Jenny Bryan, RStudio and University of British Columbia Reseña del editor If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you 'cooking from scratch', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code. Descripción del libro Designed for a new generation of biologists, this textbook teaches modern computational statistics by using R/Bioconductor to analyze experimental data from high-throughput technologies. The presentation minimizes mathematical notation and emphasizes inductive understanding from well-chosen examples, hands-on simulation, and visualization. Biografía del autor Susan Holmes is Professor of Statistics at Stanford University, California. She specializes in exploring and visualizing multidomain biological data, using computational statistics to draw inferences in microbiology, immunology and cancer biology. She has published over 100 research papers, and has been a key developer of software for the multivariate analyses of complex heterogeneous data. She was the Breiman Lecturer at NIPS 2016, has been named a Fields Institute fellow, and is currently a fellow at the Center for the Advances Study of the Behavioral Sciences.Wolfgang Huber is Research Group Leader and Senior Scientist at the European Molecular Biological Laboratory, where he develops computational methods for new biotechnologies and applies them to biological discovery. He has published over 150 research papers in functional genomics, cancer and statistical methods. He is a founding member of the open-source bioinformatics software collaboration Bioconductor and has co-authored two books on Bioconductor.
Detalles del Libro
- Name: Modern Statistics for Modern Biology
- Autor: Susan Holmes,Wolfgang Huber
- Categoria: Libros,Libros universitarios y de estudios superiores,Medicina y ciencias de la salud
- Tamaño del archivo: 18 MB
- Tipos de archivo: PDF Document
- Descargada: 125 times
- Idioma: Español
- Archivos de estado: AVAILABLE
Gratis Modern Statistics for Modern Biology de Susan Holmes,Wolfgang Huber PDF [ePub Mobi] Gratis
Introduction / Modern Statistics for Modern Biology ~ Introduction. The two instances of modern in the title of this book reflect the two major recent revolutions in biological data analyses:. Biology, formerly a science with sparse, often only qualitative data has turned into a field whose production of quantitative data is on par with high energy physics or astronomy, and whose data are wildly more heterogeneous and complex.
Home / Modern Statistics for Modern Biology ~ Modern Statistics for Modern Biology. Susan Holmes, Wolfgang Huber Chapters. Home. Book supplements; Introduction 1 Generative Models for Discrete Data 2 Statistical Modeling 3 High Quality Graphics in R 4 Mixture Models 5 Clustering 6 Testing 7 Multivariate Analysis 8 High-Throughput Count Data 9 Multivariate methods for heterogeneous data 10 .
Modern Statistics for Modern Biology / NHBS Bookstore ~ Buy Modern Statistics for Modern Biology (9781108705295): NHBS - Susan Holmes, Wolfgang Huber, Cambridge University Press
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New book: Modern Statistics for Modern Biology - Huber ~ The text book Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber has been published through Cambridge University Press (paperback). An online HTML version is also available. From the blurb: “If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book.
Modern Statistics for the Life Sciences - Alan Grafen ~ Modern Statistics for the Life Sciences Alan Grafen and Rosie Hails. Teaches the reader the language of model formulae, universally employed by statisticians today, and found in all computer statistics packages. Employs General Linear Models (GLMs), powerful tools to analyse data using a large array of methods at the same time.
Modern Statistics for Modern Biology - Biostar ~ Modern Statistics For Modern Biology is more generic while Computational Genomics with R (the book you link to) is more directly targeted at genomics. Also modern statistics has more explanations and emphasizes some of the reasoning behind the math while I get the impression that computational genomics is lighter on explanations and is more written as a guide.
Modern Statistics for Modern Biology: 9781108705295 ~ Modern Statistics for Modern Biology is not your typical statistics book in which you encounter pages of equations and mathematical proofs of the said equations, and, if you are lucky, some applications and examples in real world. Actually, this book contains almost no mathematical proofs.
Modern Applied Statistics with S Statistics and Computing ~ Modern Applied Statistics with S Statistics and Computing: : Venables, W.N., Ripley, B.D.: Libros en idiomas extranjeros
Modern Statistics for Modern Biology: Holmes, Susan, Huber ~ Modern Statistics for Modern Biology is not your typical statistics book in which you encounter pages of equations and mathematical proofs of the said equations, and, if you are lucky, some applications and examples in real world. Actually, this book contains almost no mathematical proofs.
Modern Statistics for Modern Biology: : Holmes ~ Buy Modern Statistics for Modern Biology by Holmes, Susan, Huber, Wolfgang (ISBN: 9781108705295) from 's Book Store. Everyday low prices and free delivery on eligible orders.
《Modern Statistics for Modern Biology》Chapter 三：R语言中的高质量图形 ~ 《Modern Statistics for Modern Biology》Chapter 一： 离散数据模型的预测（1.1 - 1.3） 《Modern Statistic.
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Modern Statistics for Modern Biology : Susan Holmes ~ If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2.
Modern Statistics for Modern Biology / Life Science ~ Modern Statistics for Modern Biology Collaborators: Susan Holmes & Wolfgang Huber. Background Synergies of modern biology and statistics. Biology has become a data-rich science. There are rapid developments of new technologies, and the data we can now gather about biological systems are large, heterogeneous and complex. We need to be able .
Modern Statistics for Modern Biology / Mathematical ~ In several places the reader is directed to Rice's text on mathematical statistics for details so perhaps the prerequisite could be characterized as the ability to benefit from such a reference. The reader will also need to be familiar with a great deal of modern biology.
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Modern Statistics for the Life Sciences: Learn How to ~ Modern Statistics for the Life Sciences teaches statistics in a different way. It is aimed at undergraduate students in the life sciences, and it will also be invaluable for many graduate students. It makes the powerful methods of model formulae and the General Linear Model accessible to undergraduates for the first time.
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