Reproducible Bioinformatics with Python

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Publisher : O'Reilly Media
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ISBN 10 : 1098100883
Pages : 350 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (98 users download)


Download Reproducible Bioinformatics with Python by Ken Youens-Clark PDF/Ebook Free clicking on the below button will initiate the downloading process of Reproducible Bioinformatics with Python by Ken Youens-Clark. This book is available in ePub and PDF format with a single click unlimited downloads. Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and postdoc researchers who've never learned basic programming skills. This practical guide shows how to exploit the best parts of Python for solving problems in biology while also creating documented, tested, reproducible software. Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates how to write effective Python code and how to use tests to write and refactor scientific programs. You'll learn the latest Python features and tools--such as linters, formatters, type checkers, and tests--to write documented and tested programs. Create command-line Python programs that document and validate parameters Write tests to verify refactor programs and confirm they're correct Address bioinformatics ideas using Python data structures (strings, lists, and sets) and modules such as Biopython Create reproducible shortcuts and workflows using makefiles Parse essential bioinformatics file formats such as FASTA, FASTQ, and SwissProt Find patterns of text using regular expressions Use higher-order functions in Python like filter() and map()


Mastering Python for Bioinformatics

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781098100858
Pages : 456 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.0/5 (1 users download)


Download Mastering Python for Bioinformatics by Ken Youens-Clark PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Python for Bioinformatics by Ken Youens-Clark. This book is available in ePub and PDF format with a single click unlimited downloads. Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software. Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and toolsâ??including linters, formatters, type checkers, and testsâ??to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming. Create command-line Python programs to document and validate parameters Write tests to verify refactor programs and confirm they're correct Address bioinformatics ideas using Python data structures and modules such as Biopython Create reproducible shortcuts and workflows using makefiles Parse essential bioinformatics file formats such as FASTA and FASTQ Find patterns of text using regular expressions Use higher-order functions in Python like filter(), map(), and reduce()


Mastering Python Data Visualization

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781783988334
Pages : 372 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (988 users download)


Download Mastering Python Data Visualization by Kirthi Raman PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Python Data Visualization by Kirthi Raman. This book is available in ePub and PDF format with a single click unlimited downloads. Generate effective results in a variety of visually appealing charts using the plotting packages in Python About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. What You Will Learn Gather, cleanse, access, and map data to a visual framework Recognize which visualization method is applicable and learn best practices for data visualization Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment In Detail Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis. By the end of this book, you will be able to effectively solve a broad set of data analysis problems. Style and approach The approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields


Command-Line Rust

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781098109387
Pages : 398 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.0/5 (19 users download)


Download Command-Line Rust by Ken Youens-Clark PDF/Ebook Free clicking on the below button will initiate the downloading process of Command-Line Rust by Ken Youens-Clark. This book is available in ePub and PDF format with a single click unlimited downloads. For several consecutive years, Rust has been voted "most loved programming language" in Stack Overflow's annual developer survey. This open source systems programming language is now used for everything from game engines and operating systems to browser components and virtual reality simulation engines. But Rust is also an incredibly complex language with a notoriously difficult learning curve. Rather than focusing on the language as a whole, this guide teaches Rust using a single small, complete, focused program in each chapter. Author Ken Youens-Clark shows you how to start, write, and test each of these programs to create a finished product. You'll learn how to handle errors in Rust, read and write files, and use regular expressions, Rust types, structs, and more. Discover how to: Use Rust's standard libraries and data types such as numbers, strings, vectors, structs, Options, and Results to create command-line programs Write and test Rust programs and functions Read and write files, including stdin, stdout, and stderr Document and validate command-line arguments Write programs that fail gracefully Parse raw and delimited text manually, using regular expressions and Rust crates Use and control randomness


Mastering Python for Data Science

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781784392628
Pages : 294 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (392 users download)


Download Mastering Python for Data Science by Samir Madhavan PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Python for Data Science by Samir Madhavan. This book is available in ePub and PDF format with a single click unlimited downloads. Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.


Mastering Python Scientific Computing

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781783288830
Pages : 300 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (288 users download)


Download Mastering Python Scientific Computing by Hemant Kumar Mehta PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Python Scientific Computing by Hemant Kumar Mehta. This book is available in ePub and PDF format with a single click unlimited downloads. A complete guide for Python programmers to master scientific computing using Python APIs and tools About This Book The basics of scientific computing to advanced concepts involving parallel and large scale computation are all covered. Most of the Python APIs and tools used in scientific computing are discussed in detail The concepts are discussed with suitable example programs Who This Book Is For If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming. What You Will Learn Fundamentals and components of scientific computing Scientific computing data management Performing numerical computing using NumPy and SciPy Concepts and programming for symbolic computing using SymPy Using the plotting library matplotlib for data visualization Data analysis and visualization using Pandas, matplotlib, and IPython Performing parallel and high performance computing Real-life case studies and best practices of scientific computing In Detail In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing. At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python. The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs. Style and approach This book follows a hands-on approach to explain the complex concepts related to scientific computing. It details various APIs using appropriate examples.


Command-Line Rust

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781098109400
Pages : 398 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.0/5 (19 users download)


Download Command-Line Rust by Ken Youens-Clark PDF/Ebook Free clicking on the below button will initiate the downloading process of Command-Line Rust by Ken Youens-Clark. This book is available in ePub and PDF format with a single click unlimited downloads. For several consecutive years, Rust has been voted "most loved programming language" in Stack Overflow's annual developer survey. This open source systems programming language is now used for everything from game engines and operating systems to browser components and virtual reality simulation engines. But Rust is also an incredibly complex language with a notoriously difficult learning curve. Rather than focusing on the language as a whole, this guide teaches Rust using a single small, complete, focused program in each chapter. Author Ken Youens-Clark shows you how to start, write, and test each of these programs to create a finished product. You'll learn how to handle errors in Rust, read and write files, and use regular expressions, Rust types, structs, and more. Discover how to: Use Rust's standard libraries and data types such as numbers, strings, vectors, structs, Options, and Results to create command-line programs Write and test Rust programs and functions Read and write files, including stdin, stdout, and stderr Document and validate command-line arguments Write programs that fail gracefully Parse raw and delimited text manually, using regular expressions and Rust crates Use and control randomness


Mastering Numerical Computing with NumPy

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781788996846
Pages : 248 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (996 users download)


Download Mastering Numerical Computing with NumPy by Umit Mert Cakmak PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Numerical Computing with NumPy by Umit Mert Cakmak. This book is available in ePub and PDF format with a single click unlimited downloads. Enhance the power of NumPy and start boosting your scientific computing capabilities Key Features Grasp all aspects of numerical computing and understand NumPy Explore examples to learn exploratory data analysis (EDA), regression, and clustering Access NumPy libraries and use performance benchmarking to select the right tool Book Description NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations. What you will learn Perform vector and matrix operations using NumPy Perform exploratory data analysis (EDA) on US housing data Develop a predictive model using simple and multiple linear regression Understand unsupervised learning and clustering algorithms with practical use cases Write better NumPy code and implement the algorithms from scratch Perform benchmark tests to choose the best configuration for your system Who this book is for Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book.


Systems Programming with Rust

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Publisher : O'Reilly Media
Release Date :
ISBN 10 : 1098109430
Pages : 350 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (98 users download)


Download Systems Programming with Rust by Ken Youens-Clark PDF/Ebook Free clicking on the below button will initiate the downloading process of Systems Programming with Rust by Ken Youens-Clark. This book is available in ePub and PDF format with a single click unlimited downloads. For several consecutive years, Rust has been voted "most loved programming language" in Stack Overflow's annual developer survey. This open source systems programming language is now used for everything from game engines and operating systems to browser components and virtual reality simulation engines. But Rust is also an incredibly complex language with a notoriously difficult learning curve. Rather than focus on the language as a whole, this guide teaches Rust using a single small, complete, focused program in each chapter. Author Ken Youens-Clark shows you how to start, write, and test each of these programs to create a finished product. You'll learn how to handle errors in Rust, read and write files, and use regular expressions, Rust types, structs, and more. Discover how to: Use Rust's standard libraries and data types such as strings, vectors, dictionaries, and sets to create systems programs Write and test Rust programs and functions Read and write files, including stdin, stdout, and stderr Document and validate command-line arguments Write programs that fail gracefully Parse raw and delimited text as well as Excel data Use and control randomness


Mastering Parallel Programming with R

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Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781784394622
Pages : 244 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (394 users download)


Download Mastering Parallel Programming with R by Simon R. Chapple PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Parallel Programming with R by Simon R. Chapple. This book is available in ePub and PDF format with a single click unlimited downloads. Master the robust features of R parallel programming to accelerate your data science computations About This Book Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest Become an expert in writing the most efficient and highest performance parallel algorithms in R Get to grips with the concept of parallelism to accelerate your existing R programs Who This Book Is For This book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks. What You Will Learn Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS) Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages Build and extend a parallel R package (SPRINT) with your own MPI-based routines Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them Build a task farm master-worker, spatial grid, and hybrid parallel R programs In Detail R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources. Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems. Style and approach This book leads you chapter by chapter from the easy to more complex forms of parallelism. The author's insights are presented through clear practical examples applied to a range of different problems, with comprehensive reference information for each of the R packages employed. The book can be read from start to finish, or by dipping in chapter by chapter, as each chapter describes a specific parallel approach and technology, so can be read as a standalone.


Principles of Data Science

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781785888922
Pages : 388 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (888 users download)


Download Principles of Data Science by Sinan Ozdemir PDF/Ebook Free clicking on the below button will initiate the downloading process of Principles of Data Science by Sinan Ozdemir. This book is available in ePub and PDF format with a single click unlimited downloads. Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.


Implementing Reproducible Research

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Publisher : CRC Press
Release Date :
ISBN 10 : 9781315360393
Pages : 448 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.6/5 (36 users download)


Download Implementing Reproducible Research by Victoria Stodden PDF/Ebook Free clicking on the below button will initiate the downloading process of Implementing Reproducible Research by Victoria Stodden. This book is available in ePub and PDF format with a single click unlimited downloads. In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.


Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, 8 Volume Set

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Publisher : Wiley
Release Date :
ISBN 10 : 0470849746
Pages : 4096 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.7/5 (47 users download)


Download Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, 8 Volume Set by Michael J. Dunn PDF/Ebook Free clicking on the below button will initiate the downloading process of Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, 8 Volume Set by Michael J. Dunn. This book is available in ePub and PDF format with a single click unlimited downloads. Available in print and online, this unique reference brings together all four fields of genetics, genomics, proteomics, and bioinformatics to meet your dynamic research requirements. It brings together the latest concepts in these vibrant areas and ensures a truly multidisciplinary approach. Topics include genetic variation and evolution, epigenetics, the human genome, expression profiling, proteome families, structural proteomics, gene finding/gene structure, protein function and annotation, and more. The work incorporates a vast amount of topical information, profiles cutting-edge techniques, and presents the very latest findings from an international team of over five hundred contributors. With articles for both students and more experienced scientists, this is a key reference source for everyone. Contains more than 450 articles covering all aspects of genomics, proteomics, bioinformatics and related technologies Includes a glossary containing over 550 clear and concise definitions "I am pleased to recommend it heartily as a essential reference tool…should remain the definitive work…for many years to come." THE CHEMICAL EDUCATOR “Jorde…and co-editors have done a remarkable job in coordinating this information, distilling it into a package that is both easy to navigate and over-flowing in discovery." ELECTRIC REVIEW


Mastering Go

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Publisher :
Release Date :
ISBN 10 : 1783981245
Pages : 397 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (783 users download)


Download Mastering Go by Vitor De Mario PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Go by Vitor De Mario. This book is available in ePub and PDF format with a single click unlimited downloads. Build efficient, high-performance, concurrent, and easily readable Go programsAbout This Book* Dig deep into the Go programming language* Be ready to tackle any challenge with the language* Get to grips with the functional side of Go* Master the essential http packages such as net/http and gorilla/mux as well as full-fledged web frameworksWho This Book Is ForYou should have a good grasp of the Go language syntax, have written a few programs, and know your way around the basics. If you're ready to dig deeper, this is the book for you. Don't worry-you don't need to know every nook and cranny.What You Will Learn* Create well-designed Go programs with interfaces* Master concurrency models and parallelism* Navigate your way around all styles of testing in Go* Understand how to measure performance* Choose the right tradeoff between readability and fast code* Create web servers that fit the design of the net/http package* Explore development of microservices and API generationIn DetailThe Go programming language offers a combination of easily readable and maintainable code with an intuitive concurrency model and high performance. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualized way.We start off by discussing package design, testing, and command line programming. Later, we look into the most powerful aspects of the language, such as interfaces, concurrency and parallelism, error handling, and functional programming.Moving on, we explore programming for the web, going deep into the standard library framework: net/http. After fully exploring net/http, we check complementary libraries such as gorilla/mux and negroni. Going ahead, we explore full-fledged frameworks such as Beego, Gin, Echo, and others. Microservice construction with go-kit and API generation with Swagger are also covered, preparing for the web of today.You will learn how to evaluate performance with profiling, extracting the best performance from your Go programs without sacrificing readability, and attacking the bottlenecks of your program, where it matters. We'll understand vendoring, reproducible builds, reflection, and, finally, we will teach you how to access C functions with a simple Go call.By the end of the book, you will be able to take on any challenge with the Go programming language.


Metabolic Pathway Design

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Publisher : Springer Nature
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ISBN 10 : 9783030298654
Pages : 168 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (298 users download)


Download Metabolic Pathway Design by Pablo Carbonell PDF/Ebook Free clicking on the below button will initiate the downloading process of Metabolic Pathway Design by Pablo Carbonell. This book is available in ePub and PDF format with a single click unlimited downloads. This textbook presents solid tools for in silico engineering biology, offering students a step-by-step guide to mastering the smart design of metabolic pathways. The first part explains the Design-Build-Test-Learn-cycle engineering approach to biology, discussing the basic tools to model biological and chemistry-based systems. Using these basic tools, the second part focuses on various computational protocols for metabolic pathway design, from enzyme selection to pathway discovery and enumeration. In the context of industrial biotechnology, the final part helps readers understand the challenges of scaling up and optimisation. By working with the free programming language Scientific Python, this book provides easily accessible tools for studying and learning the principles of modern in silico metabolic pathway design. Intended for advanced undergraduates and master’s students in biotechnology, biomedical engineering, bioinformatics and systems biology students, the introductory sections make it also useful for beginners wanting to learn the basics of scientific coding and find real-world, hands-on examples.


Mastering Apache Spark 2.x

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Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781785285226
Pages : 354 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (285 users download)


Download Mastering Apache Spark 2.x by Romeo Kienzler PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Apache Spark 2.x by Romeo Kienzler. This book is available in ePub and PDF format with a single click unlimited downloads. Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. Master the art of real-time processing with the help of Apache Spark 2.x Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames Learn how specific parameter settings affect overall performance of an Apache Spark cluster Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.


Mastering Kubernetes

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ISBN 10 : 1788999789
Pages : 468 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (788 users download)


Download Mastering Kubernetes by Gigi Sayfan PDF/Ebook Free clicking on the below button will initiate the downloading process of Mastering Kubernetes by Gigi Sayfan. This book is available in ePub and PDF format with a single click unlimited downloads. Exploit design, deployment, and management of large-scale containers Key Features Explore the latest features available in Kubernetes 1.10 Ensure that your clusters are always available, scalable, and up to date Master the skills of designing and deploying large clusters on various cloud platforms Book Description Kubernetes is an open source system that is used to automate the deployment, scaling, and management of containerized applications. If you are running more containers or want automated management of your containers, you need Kubernetes at your disposal. To put things into perspective, Mastering Kubernetes walks you through the advanced management of Kubernetes clusters. To start with, you will learn the fundamentals of both Kubernetes architecture and Kubernetes design in detail. You will discover how to run complex stateful microservices on Kubernetes including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backend. Using real-world use cases, you will explore the options for network configuration, and understand how to set up, operate, and troubleshoot various Kubernetes networking plugins. In addition to this, you will get to grips with custom resource development and utilization in automation and maintenance workflows. To scale up your knowledge of Kubernetes, you will encounter some additional concepts based on the Kubernetes 1.10 release, such as Promethus, Role-based access control, API aggregation, and more. By the end of this book, you'll know everything you need to graduate from intermediate to advanced level of understanding Kubernetes. What you will learn Architect a robust Kubernetes cluster for long-time operation Discover the advantages of running Kubernetes on GCE, AWS, Azure, and bare metal Understand the identity model of Kubernetes, along with the options for cluster federation Monitor and troubleshoot Kubernetes clusters and run a highly available Kubernetes Create and configure custom Kubernetes resources and use third-party resources in your automation workflows Enjoy the art of running complex stateful applications in your container environment Deliver applications as standard packages Who this book is for Mastering Kubernetes is for you if you are a system administrator or a developer who has an intermediate understanding of Kubernetes and wish to master its advanced features. Basic knowledge of networking would also be helpful. In all, this advanced-level book provides a smooth pathway to mastering Kubernetes.


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