Statistics at the Bench

Author: Martina Bremer
Publisher:
ISBN:
Format: PDF, ePub, Mobi
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Statistics at the Bench is a convenient bench-side companion for biologists, designed as a handy reference guide for elementary and intermediate statistical analyses. The expectations for biologists to have a more complete understanding of statistics are growing rapidly. New technologies and new areas of science, such as microarrays, next-generation sequencing, and proteomics, have dramatically increased the need for quantitative reasoning among biologists when designing experiments and interpreting results. Even the most routine informatics tools rely on statistical assumptions and methods that need to be appreciated if the scientific results are to be correct, understood, and exploited fully. This book is not a textbook. It is an essential handbook for working scientists. Statistics at the Bench provides a simple refresher for those who have forgotten what they once knew, and an overview for those wishing to use more quantitative reasoning in their research. Statistical methods, as well as guidelines for the interpretation of results, are explained using simple examples. Throughout the book, examples are accompanied by detailed Excel commands for easy reference.

Lab Math

Author: Dany Spencer Adams
Publisher: CSHL Press
ISBN: 9780879696344
Format: PDF, ePub
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Work at the biology bench requires an ever-increasing knowledge of mathematical methods and formulae. In Lab Math,Dany Spencer Adams has compiled the most common mathematical concepts and methods in molecular biology, and provided clear, straightforward guidance on their application to research investigations. Subjects range from basics such as scientific notation and measuring and making solutions, to more complex activities like quantifying and designing nucleic acids and analyzing protein activity. Tips on how to present mathematical data and statistical analysis are included. A reference section features useful tables, conversion charts and “plug and chug” equations for experimental procedures. This volume is an excellent, structured source of information that in many laboratories is often scattered and informally organized.

Statistics for Terrified Biologists

Author: Helmut van Emden
Publisher: John Wiley & Sons
ISBN: 1118541677
Format: PDF, Kindle
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“We highly recommend it—not just for statistically terrified biology students and faculty, but also for those who are occasionally anxious or uncertain. In addition to being a good starting point to learn statistics, it is a useful place to return to refresh your memory.” –The Quarterly Review of Biology, March 2009 "During the entire course of my Ph.D. I've been (embarrasingly) looking for a way to teach myself the fundamentals of statistical analysis. At this point in my education, I've come to realize that often times, simply knowing the basics is enough for you to properly apply even the most complex analytical methods. ‘Statistics for Terrified Biologists’ has been just such a book - it was more than worth the $40 I spent on it, and while my 'book clubs' aren't meant to be reviews, I highly recommend the book to anyone who's in a similar predicament to my own." –Carlo Artieri's Blog Book Club The typical biology student is “hardwired” to be wary of any tasks involving the application of mathematics and statistical analyses, but the plain fact is much of biology requires interpretation of experimental data through the use of statistical methods. This unique textbook aims to demystify statistical formulae for the average biology student. Written in a lively and engaging style, Statistics for Terrified Biologists draws on the author’s 30 years of lecturing experience. One of the foremost entomologists of his generation, van Emden has an extensive track record for successfully teaching statistical methods to even the most guarded of biology students. For the first time basic methods are presented using straightforward, jargon-free language. Students are taught to use simple formulae accurately to interpret what is being measured with each test and statistic, while at the same time learning to recognize overall patterns and guiding principles. Complemented by simple illustrations and useful case studies, this is an ideal statistics resource tool for undergraduate biology and environmental science students who lack confidence in their mathematical abilities.

Physical Biology of the Cell

Author: Rob Phillips
Publisher: Garland Science
ISBN: 1134111584
Format: PDF, ePub, Docs
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Physical Biology of the Cell is a textbook for a first course in physical biology or biophysics for undergraduate or graduate students. It maps the huge and complex landscape of cell and molecular biology from the distinct perspective of physical biology. As a key organizing principle, the proximity of topics is based on the physical concepts that unite a given set of biological phenomena. Herein lies the central premise: that the appropriate application of a few fundamental physical models can serve as the foundation of whole bodies of quantitative biological intuition, useful across a wide range of biological problems. The Second Edition features full-color illustrations throughout, two new chapters, a significantly expanded set of end-of-chapter problems, and is available in a variety of e-book formats.

At the Bench

Author: Kathy Barker
Publisher: CSHL Press
ISBN: 9780879697082
Format: PDF, Docs
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A clue hidden in a toy ship leads Tintin on a dangerous treasure hunt.

Bioinformatics Programming Using Python

Author: Mitchell L Model
Publisher: "O'Reilly Media, Inc."
ISBN: 9781449382902
Format: PDF, Docs
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Powerful, flexible, and easy to use, Python is an ideal language for building software tools and applications for life science research and development. This unique book shows you how to program with Python, using code examples taken directly from bioinformatics. In a short time, you'll be using sophisticated techniques and Python modules that are particularly effective for bioinformatics programming. Bioinformatics Programming Using Python is perfect for anyone involved with bioinformatics -- researchers, support staff, students, and software developers interested in writing bioinformatics applications. You'll find it useful whether you already use Python, write code in another language, or have no programming experience at all. It's an excellent self-instruction tool, as well as a handy reference when facing the challenges of real-life programming tasks. Become familiar with Python's fundamentals, including ways to develop simple applications Learn how to use Python modules for pattern matching, structured text processing, online data retrieval, and database access Discover generalized patterns that cover a large proportion of how Python code is used in bioinformatics Learn how to apply the principles and techniques of object-oriented programming Benefit from the "tips and traps" section in each chapter

Modern Statistics for Modern Biology

Author: Susan Holmes
Publisher: Cambridge University Press
ISBN: 9781108427029
Format: PDF, Kindle
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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.

Systemic Approaches in Bioinformatics and Computational Systems Biology Recent Advances

Author: Lecca, Paola
Publisher: IGI Global
ISBN: 1613504365
Format: PDF, ePub, Mobi
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The convergence of biology and computer science was initially motivated by the need to organize and process a growing number of biological observations resulting from rapid advances in experimental techniques. Today, however, close collaboration between biologists, biochemists, medical researchers, and computer scientists has also generated remarkable benefits for the field of computer science. Systemic Approaches in Bioinformatics and Computational Systems Biology: Recent Advances presents new techniques that have resulted from the application of computer science methods to the organization and interpretation of biological data. The book covers three subject areas: bioinformatics, computational biology, and computational systems biology. It focuses on recent, systemic approaches in computer science and mathematics that have been used to model, simulate, and more generally, experiment with biological phenomena at any scale.

A Handbook of Statistical Analyses using R Third Edition

Author: Torsten Hothorn
Publisher: CRC Press
ISBN: 1482204584
Format: PDF, ePub, Mobi
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Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis. New to the Third Edition Three new chapters on quantile regression, missing values, and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses Whether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.

Beginning Perl for Bioinformatics

Author: James Tisdall
Publisher: "O'Reilly Media, Inc."
ISBN: 9780596550479
Format: PDF, ePub, Docs
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With its highly developed capacity to detect patterns in data, Perl has become one of the most popular languages for biological data analysis. But if you're a biologist with little or no programming experience, starting out in Perl can be a challenge. Many biologists have a difficult time learning how to apply the language to bioinformatics. The most popular Perl programming books are often too theoretical and too focused on computer science for a non-programming biologist who needs to solve very specific problems.Beginning Perl for Bioinformatics is designed to get you quickly over the Perl language barrier by approaching programming as an important new laboratory skill, revealing Perl programs and techniques that are immediately useful in the lab. Each chapter focuses on solving a particular bioinformatics problem or class of problems, starting with the simplest and increasing in complexity as the book progresses. Each chapter includes programming exercises and teaches bioinformatics by showing and modifying programs that deal with various kinds of practical biological problems. By the end of the book you'll have a solid understanding of Perl basics, a collection of programs for such tasks as parsing BLAST and GenBank, and the skills to take on more advanced bioinformatics programming. Some of the later chapters focus in greater detail on specific bioinformatics topics. This book is suitable for use as a classroom textbook, for self-study, and as a reference.The book covers: Programming basics and working with DNA sequences and strings Debugging your code Simulating gene mutations using random number generators Regular expressions and finding motifs in data Arrays, hashes, and relational databases Regular expressions and restriction maps Using Perl to parse PDB records, annotations in GenBank, and BLAST output