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Biological Data Analysis
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Book Synopsis A Primer in Biological Data Analysis and Visualization Using R by : Gregg Hartvigsen
Download or read book A Primer in Biological Data Analysis and Visualization Using R written by Gregg Hartvigsen and published by Columbia University Press. This book was released on 2014-02-18 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Analysis of Biological Data by : Michael C. Whitlock
Download or read book The Analysis of Biological Data written by Michael C. Whitlock and published by Macmillan Higher Education. This book was released on 2019-11-22 with total page 2074 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Analysis of Biological Data provides students with a practical foundation of statistics for biology students. Every chapter has several biological or medical examples of key concepts, and each example is prefaced by a substantial description of the biological setting. The emphasis on real and interesting examples carries into the problem sets where students have dozens of practice problems based on real data. The third edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of examples and topics come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use. All the data sets, R scripts for all worked examples in the book, as well as many other teaching resources, are available to qualified instructors (see below).
Download or read book Analysis of Biological Data written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Experimental Design and Data Analysis for Biologists by : Gerald Peter Quinn
Download or read book Experimental Design and Data Analysis for Biologists written by Gerald Peter Quinn and published by Cambridge University Press. This book was released on 2002-03-21 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression, analysis of variance, correlation, graphical.
Book Synopsis Data Processing Handbook for Complex Biological Data Sources by : Gauri Misra
Download or read book Data Processing Handbook for Complex Biological Data Sources written by Gauri Misra and published by Academic Press. This book was released on 2019-03-23 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Biological Data Analysis by : John C. Fry
Download or read book Biological Data Analysis written by John C. Fry and published by Oxford University Press, USA. This book was released on 1993 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many biologists remain unfamiliar with statistical analysis and modelling, yet need to apply these techniques increasingly in their research. This volume describes how to analyze biological data, with commonly available software packages, without making errors which can invalidate results.Practical guidance is provided for planning the correct strategy for a variety of different statistical approaches and modelling problems and interpreting the results. Many examples of computer commands and output are given to illustrate the different analytical approaches. Biological DataAnalysis: A Practical Approach has been designed specifically to allow researchers with only a minimal knowledge of statistics to understand a variety of statistical methods and apply them directly. The provision of data sets from several biological disciplines will make this book useful to alltypes of biologists.
Book Synopsis Topological Data Analysis for Genomics and Evolution by : Raul Rabadan
Download or read book Topological Data Analysis for Genomics and Evolution written by Raul Rabadan and published by Cambridge University Press. This book was released on 2019-12-19 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology as well as mathematicians interested in applied topology.
Book Synopsis Analysis of Biological Networks by : Björn H. Junker
Download or read book Analysis of Biological Networks written by Björn H. Junker and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.
Book Synopsis Statistical Methods in Biology by : S.J. Welham
Download or read book Statistical Methods in Biology written by S.J. Welham and published by CRC Press. This book was released on 2014-08-22 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural scien
Book Synopsis Biological Sequence Analysis by : Richard Durbin
Download or read book Biological Sequence Analysis written by Richard Durbin and published by Cambridge University Press. This book was released on 1998-04-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
Book Synopsis Data Analytics in Bioinformatics by : Rabinarayan Satpathy
Download or read book Data Analytics in Bioinformatics written by Rabinarayan Satpathy and published by John Wiley & Sons. This book was released on 2021-01-20 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Book Synopsis Data Analysis in Biochemistry and Biophysics by : Magar Mager
Download or read book Data Analysis in Biochemistry and Biophysics written by Magar Mager and published by Elsevier. This book was released on 2012-12-02 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis in Biochemistry and Biophysics describes the techniques how to derive the most amount of quantitative and statistical information from data gathered in enzyme kinetics, protein-ligand equilibria, optical rotatory dispersion, chemical relaxation methods. This book focuses on the determination and analysis of parameters in different models that are used in biochemistry, biophysics, and molecular biology. The Michaelis-Menten equation can explain the process to obtain the maximum amount of information by determining the parameters of the model. This text also explains the fundamentals present in hypothesis testing, and the equation that represents the statistical aspects of a linear model occurring frequently in this field of testing. This book also analyzes the ultraviolet spectra of nucleic acids, particularly, to establish the composition of melting regions of nucleic acids. The investigator can use the matrix rank analysis to determine the spectra to substantiate systems whose functions are not known. This text also explains flow techniques and relaxation methods associated with rapid reactions to determine transient kinetic parameters. This book is suitable for molecular biologists, biophysicists, physiologists, biochemists, bio- mathematicians, statisticians, computer programmers, and investigators involved in related sciences
Book Synopsis Hands on Data Science for Biologists Using Python by : Yasha Hasija
Download or read book Hands on Data Science for Biologists Using Python written by Yasha Hasija and published by CRC Press. This book was released on 2021-04-08 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.
Book Synopsis Biostatistics with R by : Babak Shahbaba
Download or read book Biostatistics with R written by Babak Shahbaba and published by Springer Science & Business Media. This book was released on 2011-12-15 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.
Book Synopsis Analyzing Network Data in Biology and Medicine by : Nataša Pržulj
Download or read book Analyzing Network Data in Biology and Medicine written by Nataša Pržulj and published by Cambridge University Press. This book was released on 2019-03-28 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.
Book Synopsis Molecular Data Analysis Using R by : Csaba Ortutay
Download or read book Molecular Data Analysis Using R written by Csaba Ortutay and published by John Wiley & Sons. This book was released on 2017-02-06 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories. Key features include: • Broad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered. • First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology. • Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users.
Book Synopsis Data Analysis for the Life Sciences with R by : Rafael A. Irizarry
Download or read book Data Analysis for the Life Sciences with R written by Rafael A. Irizarry and published by CRC Press. This book was released on 2016-10-04 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
