Advanced Python for BiologistsAdvanced Python for Biologists

Advanced Python for Biologists is a programming course for workers in biology and bioinformatics who want to develop their programming skills.

Author: Martin O. Jones

Publisher: Createspace Independent Pub

ISBN: 1495244377


Page: 267

View: 964

Advanced Python for Biologists is a programming course for workers in biology and bioinformatics who want to develop their programming skills. It starts with the basic Python knowledge outlined in Python for Biologists and introduces advanced Python tools and techniques with biological examples. You'll learn: - How to use object-oriented programming to model biological entities - How to write more robust code and programs by using Python's exception system - How to test your code using the unit testing framework - How to transform data using Python's comprehensions - How to write flexible functions and applications using functional programming - How to use Python's iteration framework to extend your own object and functions Advanced Python for Biologists is written with an emphasis on practical problem-solving and uses everyday biological examples throughout. Each section contains exercises along with solutions and detailed discussion.

Python for BiologistsPython for Biologists

Python for biologists is a complete programming course for beginners that will give you the skills you need to tackle common biological and bioinformatics problems.

Author: Martin Jones

Publisher: Createspace Independent Pub

ISBN: UCR:31210023746751


Page: 229

View: 860

Python for biologists is a complete programming course for beginners that will give you the skills you need to tackle common biological and bioinformatics problems.

Effective Python Development for BiologistsEffective Python Development for Biologists

Direct applications in bioinformatics. I bought the advanced python book too." "The most useful guide to Python I've found...I've tried a few Python books, and this is by far the best for me."

Author: Martin Jones

Publisher: Createspace Independent Publishing Platform

ISBN: 153910303X


Page: 300

View: 252

Python is rapidly becoming the standard language for many talks in scientific research, and is particularly popular in biology and bioinformatics. One of the great strengths of Python is the ecosystem of tools and libraries that have grown up around it. This book introduces the novice biologist programmer to tools and techniques that make developing Python code easier and faster and will help you to write more reliable, performant programs. Written by a biologist, it focusses on solving the problems that students and researchers encounter every day: How do I make my program run faster? How can I be sure that my results are correct? How do I share this program with my colleagues? How can I speed up the process of writing my code? Chapters include: Environments for development - learn how you can take advantage of different tools for actually writing code, including those designed specifically for scientific work. Organising and sharing code - learn how Python's module and packaging system works, how to effectively reuse code across multiple projects, and how to share your programs with colleagues and the wider world. Testing - learn how automated testing can make your code more reliable, how to catch bugs before they impact your work, and how to edit code with confidence. Performance - learn how to make your code run quickly even on large datasets, how to understand the scaling behaviour of your code, and explore the trade offs involved in designing code. User interfaces - learn how to make your code more user friendly, how to design effective interfaces, and how to automate record-keeping with Python's logging system. About the author Martin started his programming career by learning Perl during the course of his PhD in evolutionary biology, and started teaching other people to program soon after. Since then he has taught introductory programming to hundreds of biologists, from undergraduates to PIs, and has maintained a philosophy that programming courses must be friendly, approachable, and practical. In his academic career, Martin mixed research and teaching at the University of Edinburgh, culminating in a two year stint as Lecturer in Bioinformatics. He now runs programming courses for biological researchers as a full time freelancer. Praise for Martin's previous books "Great, great book. I think this is the perfect book for any biologist to who wants to start learning to code with Python... I didn't know a command-line from a hole in the ground when I first opened up this book, and mere days later I was impressing my colleagues with my own DNA analysis programs." "Zero to writing useful programs in a weekend... Python for Biologists arrived last Thursday, 6/16/16, I spent the whole weekend glued to my laptop in a 2 1/2 day frenzy of coding, and I just finished it -- and came on Amazon to order the next one!" "One of the BEST coding books I've used in a long time. Direct applications in bioinformatics. I bought the advanced python book too." "The most useful guide to Python I've found...I've tried a few Python books, and this is by far the best for me."

Computing Skills for BiologistsComputing Skills for Biologists

Books Martin Jones, Python for Biologists. Available as a set of two books (novice and advanced) and online at It contains tools and exercises specifically designed for biologists. Allen B. Downey, Think Python: ...

Author: Stefano Allesina

Publisher: Princeton University Press

ISBN: 9780691182759


Page: 417

View: 535

A concise introduction to key computing skills for biologists While biological data continues to grow exponentially in size and quality, many of today’s biologists are not trained adequately in the computing skills necessary for leveraging this information deluge. In Computing Skills for Biologists, Stefano Allesina and Madlen Wilmes present a valuable toolbox for the effective analysis of biological data. Based on the authors’ experiences teaching scientific computing at the University of Chicago, this textbook emphasizes the automation of repetitive tasks and the construction of pipelines for data organization, analysis, visualization, and publication. Stressing practice rather than theory, the book’s examples and exercises are drawn from actual biological data and solve cogent problems spanning the entire breadth of biological disciplines, including ecology, genetics, microbiology, and molecular biology. Beginners will benefit from the many examples explained step-by-step, while more seasoned researchers will learn how to combine tools to make biological data analysis robust and reproducible. The book uses free software and code that can be run on any platform. Computing Skills for Biologists is ideal for scientists wanting to improve their technical skills and instructors looking to teach the main computing tools essential for biology research in the twenty-first century. Excellent resource for acquiring comprehensive computing skills Both novice and experienced scientists will increase efficiency by building automated and reproducible pipelines for biological data analysis Code examples based on published data spanning the breadth of biological disciplines Detailed solutions provided for exercises in each chapter Extensive companion website

Python for the Life SciencesPython for the Life Sciences

A Gentle Introduction to Python for Life Scientists Alexander Lancaster, Gordon Webster ... Our science and technology has now advanced to the point at which the modern biologist can study living organisms at the molecular level, ...

Author: Alexander Lancaster

Publisher: Apress

ISBN: 9781484245231


Page: 376

View: 848

Treat yourself to a lively, intuitive, and easy-to-follow introduction to computer programming in Python. The book was written specifically for biologists with little or no prior experience of writing code - with the goal of giving them not only a foundation in Python programming, but also the confidence and inspiration to start using Python in their own research. Virtually all of the examples in the book are drawn from across a wide spectrum of life science research, from simple biochemical calculations and sequence analysis, to modeling the dynamic interactions of genes and proteins in cells, or the drift of genes in an evolving population. Best of all, Python for the Life Sciences shows you how to implement all of these projects in Python, one of the most popular programming languages for scientific computing. If you are a life scientist interested in learning Python to jump-start your research, this is the book for you. What You'll Learn Write Python scripts to automate your lab calculations Search for important motifs in genome sequences Use object-oriented programming with Python Study mining interaction network data for patterns Review dynamic modeling of biochemical switches Who This Book Is For Life scientists with little or no programming experience, including undergraduate and graduate students, postdoctoral researchers in academia and industry, medical professionals, and teachers/lecturers. “A comprehensive introduction to using Python for computational biology... A lovely book with humor and perspective” -- John Novembre, Associate Professor of Human Genetics, University of Chicago and MacArthur Fellow “Fun, entertaining, witty and darn useful. A magical portal to the big data revolution” -- Sandro Santagata, Assistant Professor in Pathology, Harvard Medical School “Alex and Gordon’s enthusiasm for Python is contagious” -- Glenys Thomson Professor of Integrative Biology, University of California, Berkeley

Hands on Data Science for Biologists Using PythonHands on Data Science for Biologists Using Python

Now, Python has applications in various domains like data science, web development, data visualization, and desktop applications, ... Scikit-Learn and TensorFlow are advanced libraries for Machine Learning and deep learning ...

Author: Yasha Hasija

Publisher: CRC Press

ISBN: 9781000345483


Page: 286

View: 334

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.

A Student s Guide to Python for Physical ModelingA Student s Guide to Python for Physical Modeling

Robertson Davies Starting from the foundation in this tutorial, you may be able to get the advanced material you need for a particular problem just from Web ... Python for biologists: A complete programming course for beginners.

Author: Jesse M. Kinder

Publisher: Princeton University Press

ISBN: 9780691170503


Page: 160

View: 578

Python is a computer programming language that is rapidly gaining popularity throughout the sciences. A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This tutorial focuses on fundamentals and introduces a wide range of useful techniques, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Animation Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. Web-based resources also accompany this guide and include code samples, data sets, and more.

Hybrid Systems BiologyHybrid Systems Biology

Here, the adjective “hybrid” means in a broader sense that special attention was paid to true interdisciplinary collaborations and contributions, aimed at reaching beyond some arguably advanced Python scripting performance or abstract ...

Author: Eugenio Cinquemani

Publisher: Springer

ISBN: 9783319471518


Page: 179

View: 476

This book constitutes the refereed proceedings of the 5th International Workshop on Hybrid Systems Biology, HSB 2016, held in Grenoble, France, in October 2016. The 11 full papers presented in this book were carefully reviewed and selected from 26 submissions. They were organized and presented in 4 thematic sessions also reflected in this book: model simulation; model analysis; discrete and network modelling; stochastic modelling for biological systems.

Foundations of Theoretical Approaches in Systems BiologyFoundations of Theoretical Approaches in Systems Biology

Review: Python. Programming. for. Biology. Alberto Marin-Sanguino* Specialty Division for Systems Biotechnology, ... Finally, advanced topics like parallelization and interfacing with C will point the reader to the next level.

Author: Alberto Marin-Sanguino

Publisher: Frontiers Media SA

ISBN: 9782889456833



View: 812

If biology in the 20th century was characterized by an explosion of new technologies and experimental methods, that of the 21st has seen an equally exuberant proliferation of mathematical and computational methods that attempt to systematize and explain the abundance of available data. As we live through the consolidation of a new paradigm where experimental data goes hand in hand with computational analysis, we contemplate the challenge of fusing these two aspects of the new biology into a consistent theoretical framework. Whether systems biology will survive as a field or be washed away by the tides of future fads will ultimately depend on its success to achieve this type of synthesis. The famous quote attributed to Kurt Lewin comes to mind: "there is nothing more practical than a good theory". This book presents a wide assortment of articles on systems biology in an attempt to capture the variety of current methods in systems biology and show how they can help to find answers to the challenges of modern biology.

Advanced Data Science and Analytics with PythonAdvanced Data Science and Analytics with Python

Ever since Euler's incursion in the field, mathematicians, physicists, biologists, chemists, engineers and social scientists have found uses for graphs. Given our interest in social analysis, perhaps it is illustrative to explore some ...

Author: Jesus Rogel-Salazar

Publisher: CRC Press

ISBN: 9780429822322


Page: 384

View: 498

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.