The Analysis Of Biological Data

This second edition textbook teaches modern methods of statistics through the use of fascinating biological and medical case studies. The clear and engaging writing and practical perspective allows students to understand the analytical process behind biological data. Through the use of real world biological examples, biologists and health professionals can learn statistics in an essential manner. Authors Whitlock and Schulter have over 40 years’ experience between the two of them and therefore able to understand that students learn best through interesting examples and not overcomplicated formulas. This edition includes several unusual features that they have discovered to be helpful for effectively reaching their readers.

Introductory textbook on data analysis for biology students that includes examples and exercise sets from medical and biological literature, intuitive explanations of key concepts, and a practical approach, focusing on data rather than the mathematical foundations of statistics.

R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R.

Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing

This text emphasizes intuitive understanding rather than an over-reliance on formulas. The focus is on data and graphical displays rather than the mathematical foundations of statistics, and students do not need knowledge of mathematics beyond simple algebra.

An introduction to biological networks and methods for theiranalysis Analysis of Biological Networks is the first book of itskind to provide readers with a comprehensive introduction to thestructural analysis of biological networks at the interface ofbiology and computer science. The book begins with a brief overviewof biological networks and graph theory/graph algorithms and goeson to explore: global network properties, network centralities,network motifs, network clustering, Petri nets, signal transductionand gene regulation networks, protein interaction networks,metabolic networks, phylogenetic networks, ecological networks, andcorrelation networks. Analysis of Biological Networks is a self-containedintroduction to this important research topic, assumes no expertknowledge in computer science or biology, and is accessible toprofessionals and students alike. Each chapter concludes with asummary of main points and with exercises for readers to test theirunderstanding of the material presented. Additionally, an FTP sitewith links to author-provided data for the book is available fordeeper study. This book is suitable as a resource for researchers in computerscience, biology, bioinformatics, advanced biochemistry, and thelife sciences, and also serves as an ideal reference text forgraduate-level courses in bioinformatics and biologicalresearch.

Kniha je zaměřena na regresní modely, konkrétně jednorozměrné zobecněné lineární modely (GLM). Je určena především studentům a kolegům z biologických oborů a vyžaduje pouze základní statistické vzdělání, jakým je např. jednosemestrový kurz biostatistiky. Text knihy obsahuje nezbytné minimum statistické teorie, především však řešení 18 reálných příkladů z oblasti biologie. Každý příklad je rozpracován od popisu a stanovení cíle přes vývoj statistického modelu až po závěr. K analýze dat je použit populární a volně dostupný statistický software R. Příklady byly záměrně vybrány tak, aby upozornily na leckteré problémy a chyby, které se mohou v průběhu analýzy dat vyskytnout. Zároveň mají čtenáře motivovat k tomu, jak o statistických modelech přemýšlet a jak je používat. Řešení příkladů si může čtenář vyzkoušet sám na datech, jež jsou dodávána spolu s knihou.