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Biological data of all kinds is proliferating at an incredible rate. If humans attempt to read such data in the form of numbers and letters, they will take in the information at a snail's pace. If the information is rendered graphically, however, human analysts can assimilate it and gain insight at a much faster rate. The emphasis of this book is on the graphic representation of information-containing sequences such as DNA and amino acid sequences in order to help the human analyst find interesting and biologically relevant patterns. The editor's goal is to make this voyage through molecular biology, genetics and computer graphics as accessible to a broad audience as possible, with the inclusion of glossaries at the end of most chapters and program outlines where applicable. The book will be of most interest to biologists and computer scientists and the various large reference lists should be of interest to beginners and advanced students of biology, graphic art and computer science. Contributors have sought to find pattern and meaning in the cacophony of genetic and protein sequence data using unusual computer graphics and musical techniques.
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.
Visualizing Human Biology is a visual exploration of the major concepts of biology using the human body as the context. Students are engaged in scientific exploration and critical thinking in this product specially designed for non-science majors. Topics covered include an overview of human anatomy and physiology, nutrition, immunity and disease, cancer biology, and genetics. The aim of Visualizing Human Biology is a greater understanding, appreciation and working knowledge of biology as well as an enhanced ability to make healthy choices and informed healthcare decisions.
In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.
This volume brings together information on membrane organization and dynamics from a variety of spectroscopic, microscopic and simulation approaches, spanning a broad range of time scales. The implication of such dynamic information on membrane function in health and disease is a topic of contemporary interest. The chapters cover various aspects of membrane lipid and protein dynamics, explored using a battery of experimental and theoretical approaches. The synthesis of information and knowledge gained by utilizing multiple approaches will provide the reader with a comprehensive understanding of the underlying membrane dynamics and function, which will help to develop robust dynamic models for the understanding of membrane function in healthy and diseased states. In the last few years, crystal structures of an impressive number of membrane proteins have been reported, thanks to tremendous advances in membrane protein crystallization techniques. Some of these recently solved structures belong to the G protein-coupled receptor (GPCR) family, which are particularly difficult to crystallize due to their intrinsic flexibility. Nonetheless, these static structures do not provide the necessary information to understand the function of membrane proteins in the complex membrane milieu. This volume will address the dynamic nature of membrane proteins within the membrane and will provide the reader with an up-to date overview of the theory and practical approaches that can be used. This volume will be invaluable to researchers working in a wide range of scientific areas, from biochemistry and molecular biology to biophysics and protein science. Students of these fields will also find this volume very useful. This book will also be of great use to those who are interested in the dynamic nature of biological processes.
"This is a book about what the science of perception can tell us about visualization. There is a gold mine of information about how we see to be found in more than a century of work by vision researchers. The purpose of this book is to extract from that large body of research literature those design principles that apply to displaying information effectively"--
Over the last decade biological research has changed completely. The reductionism approach of studying only a few biological entities at a time in the past is being replaced by the study of the biological system as a whole today. This requires that existing biological knowledge (data) is made readily available. Effective integration of biological knowledge from databases scattered around the internet and other information resources (for example experimental data) is recognized as a pre-requisite for many aspects of biological research. Systems for the integration of biological knowledge have to overcome several challenges: biological data sources may contain similar or overlapping coverage and the user of such systems is faced with the challenge of generating a consensus data set or selecting the "best" data source; different access methods to databases, different data formats, different naming conventions and erroneous or missing data. To address these challenges and enable effective integration of biological knowledge, the ONDEX system was created. ONDEX provides an integrated view across biological data sources to the user. Here the basic principles behind ONDEX are presented.
Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems
A guide to machine learning approaches and their application to the analysis of biological data. An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models—and to automate the process as much as possible. In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
With the convergence of Nanotechnology, Biotechnology, Information technology and Cognitive science (NBIC) fields promising to change our competitive, operational, and employment landscape in fundamental ways, we find ourselves on the brink of a new technological and science-driven business revolution. The already emerging reality of convergence is to be found in genomics, robotics, bio-information and artificial intelligence applications, such as: • Self-assembled, self-cleaning and self-healing manufactured materials and textiles, and much stronger, lighter and more customizable structural materials, • Miniature sensors allowing unobtrusive real-time health monitoring and dramatically improved diagnosis; with greatly enhanced real time information to vehicles and drivers on the way, • New generations of supercomputers and efficient energy generators based on biological processes, • Greatly enhanced drug delivery from unprecedented control over fundamental structural properties and biocompatibility of materials. These advances are here already, or in development. And Japan, other Asian nations and Western European countries are investing heavily and moving aggressively to develop and apply NBIC technologies. Notwithstanding the passage of the 21st Century Nanotechnology Research and Development Act, significant further funding and action by both government and private industry will be critical to maintaining US scientific and industry leadership.