Introduction To Biological Data Analysis In Python
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Introduction To Biological Data Analysis In Python
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Author : Stilianos Louca
language : en
Publisher: Stilianos Louca
Release Date : 2023-03-17
Introduction To Biological Data Analysis In Python written by Stilianos Louca and has been published by Stilianos Louca this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-17 with Computers categories.
This book introduces computational data analysis in biology, using the free and popular programming language Python 3. The book targets undergraduate and graduate students in biology with an interest in computational techniques, but could also be of interest to students in other scientific disciplines such as biochemistry, environmental sciences and physics. No prior programming experience is required﹣this book is intended for the motivated novice! Readers will learn to load and analyze data and produce professional visualizations. The mathematical content is kept to a bare minimum. Examples and exercises are drawn from a wide spectrum across biology, such as epidemiology, ecology, conservation biology, neuroscience, evolution, genetics, genomics and microbiology. Many exercises use realistic datasets published in the scientific literature, such as bacterial genome sequences, animal GPS tracking data, population time series and biodiversity inventories. References to the scientific literature are provided throughout.
Managing Your Biological Data With Python
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Author : Allegra Via
language : en
Publisher: CRC Press
Release Date : 2014-03-18
Managing Your Biological Data With Python written by Allegra Via and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-18 with Computers categories.
Take Control of Your Data and Use Python with ConfidenceRequiring no prior programming experience, Managing Your Biological Data with Python empowers biologists and other life scientists to work with biological data on their own using the Python language. The book teaches them not only how to program but also how to manage their data. It shows how
Introduction To Python In Earth Science Data Analysis
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Author : Maurizio Petrelli
language : en
Publisher: Springer Nature
Release Date : 2021-09-16
Introduction To Python In Earth Science Data Analysis written by Maurizio Petrelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-16 with Science categories.
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
Introduction To Biomedical Data Science
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Author : Robert Hoyt
language : en
Publisher: Lulu.com
Release Date : 2019-11-24
Introduction To Biomedical Data Science written by Robert Hoyt and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-24 with Science categories.
Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises.
Modern Python Bio Informatics
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Author : Dr. Amarendra Alluri
language : en
Publisher: RK Publication
Release Date : 2024-09-20
Modern Python Bio Informatics written by Dr. Amarendra Alluri and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-20 with Computers categories.
Modern Python Bioinformatics is an insightful guide merging Python programming with bioinformatics, designed for both beginners and seasoned professionals in computational biology. This book covers essential Python skills and advanced bioinformatics concepts, including DNA/RNA sequencing, protein structure analysis, and data visualization. It emphasizes practical applications with examples and projects that demonstrate how to handle biological data, perform statistical analyses, and develop efficient bioinformatics workflows. With accessible explanations and code snippets, it equips readers to tackle real-world challenges in bioinformatics research and development.
Hands On Data Science For Biologists Using Python
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Author : Yasha Hasija
language : en
Publisher: CRC Press
Release Date : 2021-04-08
Hands On Data Science For Biologists Using Python written by Yasha Hasija and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-08 with Computers categories.
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 Primer In Biological Data Analysis And Visualization Using R
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Author : Gregg Hartvigsen
language : en
Publisher: Columbia University Press
Release Date : 2021-06-29
A Primer In Biological Data Analysis And Visualization Using R written by Gregg Hartvigsen and has been published by Columbia University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-29 with Science categories.
R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen’s extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of correctly entering and analyzing data and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normally distributed data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter outlining algorithms and the art of programming using R. This second edition has been revised to be current with the versions of R software released since the book’s original publication. It features updated terminology, sources, and examples throughout.
Python For Bioinformatics
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Author : Dr. Parul Verma
language : en
Publisher: BPB Publications
Release Date : 2024-12-07
Python For Bioinformatics written by Dr. Parul Verma and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-07 with Computers categories.
DESCRIPTION Python for Bioinformatics is an essential resource for anyone looking to integrate programming into their biological research. As the field of bioinformatics continues to expand, the ability to analyze vast amounts of biological data becomes increasingly crucial. This book introduces bioinformatics, blending Python programming with biological data analysis. It covers essentials like string handling, regular expressions, file I/O, and object-oriented programming. You will explore Biopython for sequence alignment, format parsing, and accessing biological databases. Learn data visualization with Matplotlib and apply bioinformatics techniques like sequence alignment and phylogenetic analysis. The final chapter includes hands-on mini-projects to solidify your understanding, making it a practical guide for mastering bioinformatics and Python in real-world applications. By bridging the gap between biology and computational science, this book empowers readers to tackle real-world challenges in their learning. With clear explanations and practical examples, readers will be well-prepared to apply Python for understanding biological data, contributing to advancements in the field of bioinformatics. KEY FEATURES ● Comprehensive Python guide tailored for bioinformatics applications. ● Hands-on projects to solidify concepts and enhance practical skills. ● In-depth exploration of Python for its efficient use in bioinformatics. WHAT YOU WILL LEARN ● Understand core Python programming concepts for data analysis. ● Manipulate and analyze biological data effectively using Python. ● Create and manage functions and modules in Python code. ● Visualize complex datasets to identify patterns and insights. ● Implement file-handling techniques for various data types. ● Apply programming skills to real-world bioinformatics projects. WHO THIS BOOK IS FOR This book is ideal for students, researchers, and professionals in fields like biology, computer science, and biotechnology who seek to understand and apply bioinformatics techniques to analyze biological data and solve real-world problems. TABLE OF CONTENTS 1. Introduction to Bioinformatics and its Applications 2. Bioinformatics and its Use Cases 3. Introduction to Python and Basic Programming 4. String Handling, Modular Programming, and Data Structures 5. File Handling and Object Oriented Concept 6. Basic Concept of Biopython Module 7. Pattern Matching with Regular Expression 8. Data Handling and Visualization in Bioinformatics 9. Mini Applications in Bioinformatics 10. Mini Projects on Bioinformatics
Python For Bioinformatics
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Author : Sebastian Bassi
language : en
Publisher: CRC Press
Release Date : 2017-08-07
Python For Bioinformatics written by Sebastian Bassi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-07 with Science categories.
In today's data driven biology, programming knowledge is essential in turning ideas into testable hypothesis. Based on the author’s extensive experience, Python for Bioinformatics, Second Edition helps biologists get to grips with the basics of software development. Requiring no prior knowledge of programming-related concepts, the book focuses on the easy-to-use, yet powerful, Python computer language. This new edition is updated throughout to Python 3 and is designed not just to help scientists master the basics, but to do more in less time and in a reproducible way. New developments added in this edition include NoSQL databases, the Anaconda Python distribution, graphical libraries like Bokeh, and the use of Github for collaborative development.
Genomics Data Analysis For Crop Improvement
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Author : Priyanka Anjoy
language : en
Publisher: Springer Nature
Release Date : 2024-01-09
Genomics Data Analysis For Crop Improvement written by Priyanka Anjoy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Technology & Engineering categories.
This book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies. Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.