Large Scale Data Handling in Biology
| Author | : |
| Publisher | : Bookboon |
| Total Pages | : 53 |
| Release | : |
| Genre | : |
| ISBN | : 8776815552 |
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Large Scale Data Handling In Biology PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Large Scale Data Handling In Biology PDF full book. Access full book title Large Scale Data Handling In Biology.
Large Scale Data Analytics
| Author | : Chung Yik Cho |
| Publisher | : Springer |
| Total Pages | : 89 |
| Release | : 2019-01-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3030038920 |
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This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.
Bioinformatics Data Skills
| Author | : Vince Buffalo |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 538 |
| Release | : 2015-07 |
| Genre | : Computers |
| ISBN | : 1449367518 |
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Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, youâ??ll learn how to use freely available open source tools to extract meaning from large complex biological data sets. At no other point in human history has our ability to understand lifeâ??s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, youâ??re ready to get started. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles
Analysis Of Biological Data: A Soft Computing Approach
| Author | : Sanghamitra Bandyopadhyay |
| Publisher | : World Scientific |
| Total Pages | : 353 |
| Release | : 2007-09-03 |
| Genre | : Computers |
| ISBN | : 9814475122 |
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Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers.This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter.
Big Data Analytics in Bioinformatics and Healthcare
| Author | : Wang, Baoying |
| Publisher | : IGI Global |
| Total Pages | : 552 |
| Release | : 2014-10-31 |
| Genre | : Computers |
| ISBN | : 1466666129 |
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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.
Hands on Data Science for Biologists Using Python
| Author | : Yasha Hasija |
| Publisher | : CRC Press |
| Total Pages | : 299 |
| Release | : 2021-04-08 |
| Genre | : Computers |
| ISBN | : 1000345483 |
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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.
Computational Biology
| Author | : Röbbe Wünschiers |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 453 |
| Release | : 2013-01-30 |
| Genre | : Science |
| ISBN | : 3642347495 |
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This greatly expanded 2nd edition provides a practical introduction to - data processing with Linux tools and the programming languages AWK and Perl - data management with the relational database system MySQL, and - data analysis and visualization with the statistical computing environment R for students and practitioners in the life sciences. Although written for beginners, experienced researchers in areas involving bioinformatics and computational biology may benefit from numerous tips and tricks that help to process, filter and format large datasets. Learning by doing is the basic concept of this book. Worked examples illustrate how to employ data processing and analysis techniques, e.g. for - finding proteins potentially causing pathogenicity in bacteria, - supporting the significance of BLAST with homology modeling, or - detecting candidate proteins that may be redox-regulated, on the basis of their structure. All the software tools and datasets used are freely available. One section is devoted to explaining setup and maintenance of Linux as an operating system independent virtual machine. The author's experiences and knowledge gained from working and teaching in both academia and industry constitute the foundation for this practical approach.
Realtime Analysis of Large-Scale Data
| Author | : Daniel Becker |
| Publisher | : |
| Total Pages | : 120 |
| Release | : 2020-10-09 |
| Genre | : Computers |
| ISBN | : 9781013281990 |
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In photon science more and more data are taken. It is not possible anymore to store and process all data offline. In this book, we explore strategies for handling this large amount of data. A neural network as well as techniques from image processing are used to efficiently categorize and select useful data. We also indicate why many sophisticated algorithms cannot be used in this context. In addition, a prototype for data selection is presented, discussed, and benchmarked. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Catalyzing Inquiry at the Interface of Computing and Biology
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 469 |
| Release | : 2006-01-01 |
| Genre | : Science |
| ISBN | : 030909612X |
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Advances in computer science and technology and in biology over the last several years have opened up the possibility for computing to help answer fundamental questions in biology and for biology to help with new approaches to computing. Making the most of the research opportunities at the interface of computing and biology requires the active participation of people from both fields. While past attempts have been made in this direction, circumstances today appear to be much more favorable for progress. To help take advantage of these opportunities, this study was requested of the NRC by the National Science Foundation, the Department of Defense, the National Institutes of Health, and the Department of Energy. The report provides the basis for establishing cross-disciplinary collaboration between biology and computing including an analysis of potential impediments and strategies for overcoming them. The report also presents a wealth of examples that should encourage students in the biological sciences to look for ways to enable them to be more effective users of computing in their studies.
Experimental Design and Data Analysis for Biologists
| Author | : Gerry P. Quinn |
| Publisher | : Cambridge University Press |
| Total Pages | : 851 |
| Release | : 2002-03-21 |
| Genre | : Nature |
| ISBN | : 1139432893 |
Download Experimental Design and Data Analysis for Biologists Book in PDF, ePub and Kindle
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
