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Author: Jamie Waese
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Researchers have access to an ever-growing volume of data available at multiple levels of biological analysis. Many visual analytic tools have been developed to display a variety of biological data types but many of these tools are challenging to use and only examine one biological level of analysis at a time. The development and testing of hypotheses is difficult when the information is hard to integrate and laborious to interpret. The application of data visualization principles and user experience design best practices could improve systems biology research workflows by providing visual analytic tools with what is known in the information visualization community as a "transparent" user interface. This thesis consists of four papers that explore two central questions: 1) What is the best way to represent biological information at different levels of analysis? and 2) How do we enable researchers to explore and interact with their data as naturally and intuitively as possible? The first paper describes, ePlant, a tool for visualizing multiple levels of data that was developed using an agile process that included several rounds of user testing. The second paper presents Gene Slider, a tool for visualizing the conservation and entropy of orthologous DNA and protein sequences using a data visualization paradigm that takes better advantage of preattentive visual processing than current methods. The third paper describes Topo-phylogeny, a tool for visualizing phylogenetic relationships using a topographic map visualization paradigm that requires less cognitive processing to interpret than traditional tree diagrams. The final paper demonstrates the importance of user testing when developing a "rapid serial visual presentation" interface for identifying genes of interest using electronic fluorescent pictographs. Together these papers illustrate the complexities and benefits of applying data visualization principles and user experience design best practices to building data visualization tools for the analysis of large biological data sets. Given that hypothesis generation is fundamentally a creative process, any tools or techniques that can help researchers consider their data at a deeper level should be valuable to the scientific community.
Author: Jamie Waese
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Researchers have access to an ever-growing volume of data available at multiple levels of biological analysis. Many visual analytic tools have been developed to display a variety of biological data types but many of these tools are challenging to use and only examine one biological level of analysis at a time. The development and testing of hypotheses is difficult when the information is hard to integrate and laborious to interpret. The application of data visualization principles and user experience design best practices could improve systems biology research workflows by providing visual analytic tools with what is known in the information visualization community as a "transparent" user interface. This thesis consists of four papers that explore two central questions: 1) What is the best way to represent biological information at different levels of analysis? and 2) How do we enable researchers to explore and interact with their data as naturally and intuitively as possible? The first paper describes, ePlant, a tool for visualizing multiple levels of data that was developed using an agile process that included several rounds of user testing. The second paper presents Gene Slider, a tool for visualizing the conservation and entropy of orthologous DNA and protein sequences using a data visualization paradigm that takes better advantage of preattentive visual processing than current methods. The third paper describes Topo-phylogeny, a tool for visualizing phylogenetic relationships using a topographic map visualization paradigm that requires less cognitive processing to interpret than traditional tree diagrams. The final paper demonstrates the importance of user testing when developing a "rapid serial visual presentation" interface for identifying genes of interest using electronic fluorescent pictographs. Together these papers illustrate the complexities and benefits of applying data visualization principles and user experience design best practices to building data visualization tools for the analysis of large biological data sets. Given that hypothesis generation is fundamentally a creative process, any tools or techniques that can help researchers consider their data at a deeper level should be valuable to the scientific community.
Author: Martin Jones
Publisher:
ISBN:
Category :
Languages : en
Pages : 398
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Book Description
In biological research, we''re currently in a golden age of data. It''s never been easier to assemble large datasets to probe biological questions. But these large datasets come with their own problems. How to clean and validate data? How to combine datasets from multiple sources? And how to look for patterns in large, complex datasets and display your findings? The solution to these problems comes in the form of Python''s scientific software stack. The combination of a friendly, expressive language and high quality packages makes a fantastic set of tools for data exploration. But the packages themselves can be hard to get to grips with. It''s difficult to know where to get started, or which sets of tools will be most useful. Learning to use Python effectively for data exploration is a superpower that you can learn. With a basic knowledge of Python, pandas (for data manipulation) and seaborn (for data visualization) you''ll be able to understand complex datasets quickly and mine them for biological insight. You''ll be able to make beautiful, informative charts for posters, papers and presentations, and rapidly update them to reflect new data or test new hypotheses. You''ll be able to quickly make sense of datasets from other projects and publications - millions of rows of data will no longer be a scary prospect! In this book, Dr. Jones draws on years of teaching experience to give you the tools you need to answer your research questions. Starting with the basics, you''ll learn how to use Python, pandas, seaborn and matplotlib effectively using biological examples throughout. Rather than overwhelm you with information, the book concentrates on the tools most useful for biological data. Full color illustrations show hundreds of examples covering dozens of different chart types, with complete code samples that you can tweak and use for your own work. This book will help you get over the most common obstacles when getting started with data exploration in Python. You''ll learn about pandas'' data model; how to deal with errors in input files and how to fit large datasets in memory. The chapters on visualization will show you how to make sophisticated charts with minimal code; how to best use color to make clear charts, and how to deal with visualization problems involving large numbers of data points. Chapters include: Getting data into pandas: series and dataframes, CSV and Excel files, missing data, renaming columns Working with series: descriptive statistics, string methods, indexing and broadcasting Filtering and selecting: boolean masks, selecting in a list, complex conditions, aggregation Plotting distributions: histograms, scatterplots, custom columns, using size and color Special scatter plots: using alpha, hexbin plots, regressions, pairwise plots Conditioning on categories: using color, size and marker, small multiples Categorical axes:strip/swarm plots, box and violin plots, bar plots and line charts Styling figures: aspect, labels, styles and contexts, plotting keywords Working with color: choosing palettes, redundancy, highlighting categories Working with groups: groupby, types of categories, filtering and transforming Binning data: creating categories, quantiles, reindexing Long and wide form: tidying input datasets, making summaries, pivoting data Matrix charts: summary tables, heatmaps, scales and normalization, clustering Complex data files: cleaning data, merging and concatenating, reducing memory FacetGrids: laying out multiple charts, custom charts, multiple heat maps Unexpected behaviours: bugs and missing groups, fixing odd scales High performance pandas: vectorization, timing and sampling Further reading: dates and times, alternative syntax
Author: Wang, Baoying
Publisher: IGI Global
ISBN: 1466666129
Category : Computers
Languages : en
Pages : 552
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Book Description
As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.
Author: Alex Koszycki
Publisher:
ISBN:
Category : Bioinformatics
Languages : en
Pages : 302
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Book Description
Glue is a data visualization tool designed for exploratory analysis that allows users to interactively explore relationships and patterns in large multidimensional datasets. Users can construct scatter plots and histograms, select regions of interest, and have their selections propagated across other visualizations and even across multiple files. This powerful functionality, known as data brushing, is immensely useful in teasing out hidden relationships in large complex datasets. Originally developed for astronomical information, we have subsequently extended its use with common biological data-types and visualizations. This project will present and discuss the addition of features designed for visualizing longitudinal time-series datasets and genetic sequences, both of which are common data-types in biological processes. These features will be illustrated in a research case study investigating how sequence variants of the human immunodeficiency virus type 1 (HIV-1) affect clinical outcomes. The implemented features will be discussed in the context of alternative solutions and broad impact.
Author: Claus O. Wilke
Publisher: O'Reilly Media
ISBN: 1492031054
Category : Computers
Languages : en
Pages : 390
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Book Description
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story
Author: Lars Linsen
Publisher: Springer Science & Business Media
ISBN: 3642216080
Category : Mathematics
Languages : en
Pages : 285
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Book Description
For some time, medicine has been an important driver for the development of data processing and visualization techniques. Improved technology offers the capacity to generate larger and more complex data sets related to imaging and simulation. This, in turn, creates the need for more effective visualization tools for medical practitioners to interpret and utilize data in meaningful ways. The first edition of Visualization in Medicine and Life Sciences (VMLS) emerged from a workshop convened to explore the significant data visualization challenges created by emerging technologies in the life sciences. The workshop and the book addressed questions of whether medical data visualization approaches can be devised or improved to meet these challenges, with the promise of ultimately being adopted by medical experts. Visualization in Medicine and Life Sciences II follows the second international VMLS workshop, held in Bremerhaven, Germany, in July 2009. Internationally renowned experts from the visualization and driving application areas came together for this second workshop. The book presents peer-reviewed research and survey papers which document and discuss the progress made, explore new approaches to data visualization, and assess new challenges and research directions.
Author: Jan Taubert
Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
ISBN: 9783838129297
Category :
Languages : en
Pages : 248
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Book Description
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.
Author: Francois Ferre
Publisher: Springer Science & Business Media
ISBN: 1461241642
Category : Medical
Languages : en
Pages : 379
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Book Description
Geneticists and molecular biologists have been interested in quantifying genes and their products for many years and for various reasons (Bishop, 1974). Early molecular methods were based on molecular hybridization, and were devised shortly after Marmur and Doty (1961) first showed that denaturation of the double helix could be reversed - that the process of molecular reassociation was exquisitely sequence dependent. Gillespie and Spiegelman (1965) developed a way of using the method to titrate the number of copies of a probe within a target sequence in which the target sequence was fixed to a membrane support prior to hybridization with the probe - typically a RNA. Thus, this was a precursor to many of the methods still in use, and indeed under development, today. Early examples of the application of these methods included the measurement of the copy numbers in gene families such as the ribosomal genes and the immunoglo bulin family. Amplification of genes in tumors and in response to drug treatment was discovered by this method. In the same period, methods were invented for estimating gene num bers based on the kinetics of the reassociation process - the so-called Cot analysis. This method, which exploits the dependence of the rate of reassociation on the concentration of the two strands, revealed the presence of repeated sequences in the DNA of higher eukaryotes (Britten and Kohne, 1968). An adaptation to RNA, Rot analysis (Melli and Bishop, 1969), was used to measure the abundance of RNAs in a mixed population.
Author: Jae K. Lee
Publisher: John Wiley & Sons
ISBN: 1118211529
Category : Medical
Languages : en
Pages : 337
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Book Description
This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.
Author: Antony Unwin
Publisher: Springer Science & Business Media
ISBN: 0387379770
Category : Computers
Languages : en
Pages : 276
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Book Description
This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets and the importance of interpreting displays effectively is emphasized. Graphics should be drawn to convey information and the book includes many insightful examples. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. The book is accessible to readers with some experience of drawing statistical graphics.
