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| data science in biotechnology: Hands on Data Science for Biologists Using Python Yasha Hasija, Rajkumar Chakraborty, 2021-04-08 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. |
| data science in biotechnology: Machine Learning in Biotechnology and Life Sciences Saleh Alkhalifa, 2022-01-28 Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key FeaturesLearn the applications of machine learning in biotechnology and life science sectorsDiscover exciting real-world applications of deep learning and natural language processingUnderstand the general process of deploying models to cloud platforms such as AWS and GCPBook Description The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You'll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP. What you will learnGet started with Python programming and Structured Query Language (SQL)Develop a machine learning predictive model from scratch using PythonFine-tune deep learning models to optimize their performance for various tasksFind out how to deploy, evaluate, and monitor a model in the cloudUnderstand how to apply advanced techniques to real-world dataDiscover how to use key deep learning methods such as LSTMs and transformersWho this book is for This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book. |
| data science in biotechnology: Applied Predictive Modeling Max Kuhn, Kjell Johnson, 2013-05-17 Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. |
| data science in biotechnology: The Digital Cell Stephen J. Royle, 2019 Cell biology is becoming an increasingly quantitative field, as technical advances mean researchers now routinely capture vast amounts of data. This handbook is an essential guide to the computational approaches, image processing and analysis techniques, and basic programming skills that are now part of the skill set of anyone working in the field-- |
| data science in biotechnology: Genomics in the Cloud Geraldine A. Van der Auwera, Brian D. O'Connor, 2020-04-02 Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytesâ??or over 50 million gigabytesâ??of genomic data, and theyâ??re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian Oâ??Connor of the UC Santa Cruz Genomics Institute, guide you through the process. Youâ??ll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra |
| data science in biotechnology: Preparing for Future Products of Biotechnology National Academies of Sciences, Engineering, and Medicine, Division on Earth and Life Studies, Board on Chemical Sciences and Technology, Board on Agriculture and Natural Resources, Board on Life Sciences, Committee on Future Biotechnology Products and Opportunities to Enhance Capabilities of the Biotechnology Regulatory System, 2017-07-28 Between 1973 and 2016, the ways to manipulate DNA to endow new characteristics in an organism (that is, biotechnology) have advanced, enabling the development of products that were not previously possible. What will the likely future products of biotechnology be over the next 5â€10 years? What scientific capabilities, tools, and/or expertise may be needed by the regulatory agencies to ensure they make efficient and sound evaluations of the likely future products of biotechnology? Preparing for Future Products of Biotechnology analyzes the future landscape of biotechnology products and seeks to inform forthcoming policy making. This report identifies potential new risks and frameworks for risk assessment and areas in which the risks or lack of risks relating to the products of biotechnology are well understood. |
| data science in biotechnology: Statistics for Biotechnology Process Development Todd Coffey, Harry Yang, 2018-05-16 Written specifically for biotechnology scientists, engineers, and quality professionals, this book describes and demonstrates the proper application of statistical methods throughout Chemistry, Manufacturing, and Controls (CMC). Filled with case studies, examples, and easy-to-follow explanations of how to perform statistics in modern software, it is the first book on CMC statistics written primarily for practitioners. While statisticians will also benefit from this book, it is written particularly for industry professionals who don’t have access to a CMC statistician or who want to be more independent in the design and analysis of their experiments. Provides an introduction to the statistical concepts important in the biotechnology industry Focuses on concepts with theoretical details kept to a minimum Includes lots of real examples and case studies to illustrate the methods Uses JMP software for implementation of the methods Offers a text suitable for scientists in the industry with some quantitative training Written and edited by seasoned veterans of the biotechnology industry, this book will prove useful to a wide variety of biotechnology professionals. The book brings together individual chapters that showcase the use of statistics in the most salient areas of CMC. |
| data science in biotechnology: Applied Mycology Mahendra Rai, P. D. Bridge, 2009 The fungal kingdom consists of a wide variety of organisms with a diverse range of forms and functions. Fungi have been utilized for thousands of years and their importance in agriculture, medicine, food production and the environmental sciences is well known. New advances in genomic and metabolomic technologies have allowed further developments in the use of fungi in industry and medicine, increasing the need for a compilation of new applications, developments and technologies across the mycological field. Applied Mycology brings together a range of contributions, highlighting the diverse nature of current research. Chapters include discussions of fungal associations in the environment, agriculture and forestry, long established and novel applications of fungi in fermentation, the use of fungi in the pharmaceutical industry, the growing recognition of fungal infections, current interests in the use fungal enzymes in biotechnology and the new and emerging field of myconanotechnology. Demonstrating the broad coverage and importance of mycological research, this book will be of interest to researchers and students in all biological sciences. |
| data science in biotechnology: Career Opportunities in Biotechnology and Drug Development Toby Freedman, 2008 An essential guide for students in the life sciences, established researchers, and career counselors, this resource features discussions of job security, future trends, and potential career paths. Even those already working in the industry will find helpful information on how to take advantage of opportunities within their own companies and elsewhere. |
| data science in biotechnology: Biotechnology for Beginners Reinhard Renneberg, 2023-01-16 Biotechnology for Beginners, Third Edition presents the latest developments in the evolving field of biotechnology which has grown to such an extent over the past few years that increasing numbers of professional's work in areas that are directly impacted by the science. This book offers an exciting and colorful overview of biotechnology for professionals and students in a wide array of the life sciences, including genetics, immunology, biochemistry, agronomy and animal science. This book will also appeals to lay readers who do not have a scientific background but are interested in an entertaining and informative introduction to the key aspects of biotechnology. Authors Renneberg and Loroch discuss the opportunities and risks of individual technologies and provide historical data in easy-to-reference boxes, highlighting key topics. The book covers all major aspects of the field, from food biotechnology to enzymes, genetic engineering, viruses, antibodies, and vaccines, to environmental biotechnology, transgenic animals, analytical biotechnology, and the human genome. - Covers the whole of biotechnology - Presents an extremely accessible style, including lavish and humorous illustrations throughout - Includes new chapters on CRISPR cas-9, COVID-19, the biotechnology of cancer, and more |
| data science in biotechnology: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| data science in biotechnology: Biotechnology in Medical Sciences Firdos Alam Khan, 2014-05-08 As the field of medical biotechnology grows with new products and discoveries, so does the need for a holistic view of biotechnology in medicine. Biotechnology in Medical Sciences fulfills that need by delivering a detailed overview of medical biotechnology as it relates to human diseases and epidemiology, bacteriology and antibiotics, virology and vaccines, immunology and monoclonal antibodies, recombinant DNA technology and therapeutic proteins, stem cell technology, tissue engineering, molecular diagnostics and forensic science, gene therapy, synthetic biology and nanomedicine, pharmacogenomics, bioethics, biobusiness and intellectual property rights, and career opportunities. Organized to follow the chronology of major medical biotechnology research, breakthroughs, and events, this first-of-its-kind text: Covers all aspects of medical biotechnology, from labs to clinics and basic to advanced applications Describes historical perspectives and modern discoveries in medical biotechnology Explains how various biotechnology products are used to treat and prevent disease Discusses the tools and techniques currently employed in medical biotechnology Includes a bibliography at the end of each chapter to encourage further study Complete with colorful illustrations and examples, Biotechnology in Medical Sciences provides a comprehensive yet accessible treatment of this growing field. |
| data science in biotechnology: Biotechnology Mehdi Khosrowpour, Information Resources Management Association, 2019 Biotechnology can be defined as the manipulation of biological process, systems, and organisms in the production of various products. With applications in a number of fields such as biomedical, chemical, mechanical, and civil engineering, research on the development of biologically inspired materials is essential to further advancement. Biotechnology: Concepts, Methodologies, Tools, and Applications is a vital reference source for the latest research findings on the application of biotechnology in medicine, engineering, agriculture, food production, and other areas. It also examines the economic impacts of biotechnology use. Highlighting a range of topics such as pharmacogenomics, biomedical engineering, and bioinformatics, this multi-volume book is ideally designed for engineers, pharmacists, medical professionals, practitioners, academicians, and researchers interested in the applications of biotechnology. |
| data science in biotechnology: Biotechnology Entrepreneurship Craig Shimasaki, 2014-04-08 As an authoritative guide to biotechnology enterprise and entrepreneurship, Biotechnology Entrepreneurship and Management supports the international community in training the biotechnology leaders of tomorrow. Outlining fundamental concepts vital to graduate students and practitioners entering the biotech industry in management or in any entrepreneurial capacity, Biotechnology Entrepreneurship and Management provides tested strategies and hard-won lessons from a leading board of educators and practitioners. It provides a 'how-to' for individuals training at any level for the biotech industry, from macro to micro. Coverage ranges from the initial challenge of translating a technology idea into a working business case, through securing angel investment, and in managing all aspects of the result: business valuation, business development, partnering, biological manufacturing, FDA approvals and regulatory requirements. An engaging and user-friendly style is complemented by diverse diagrams, graphics and business flow charts with decision trees to support effective management and decision making. - Provides tested strategies and lessons in an engaging and user-friendly style supplemented by tailored pedagogy, training tips and overview sidebars - Case studies are interspersed throughout each chapter to support key concepts and best practices. - Enhanced by use of numerous detailed graphics, tables and flow charts |
| data science in biotechnology: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data science in biotechnology: The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry Stephanie K. Ashenden, 2021-04-23 The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient's life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. - Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research - Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved - Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide |
| data science in biotechnology: Managing Biotechnology Francoise Simon, Glen Giovannetti, 2017-10-16 A comprehensive overview of the new business context for biopharma companies, featuring numerous case studies and state-of-the-art marketing models Biotechnology has developed into a key innovation driver especially in the field of human healthcare. But as the biopharma industry continues to grow and expand its reach, development costs are colliding with aging demographics and cost-containment policies of private and public payers. Concurrently, the development and increased affordability of sophisticated digital technologies has fundamentally altered many industries including healthcare. The arrival of new information technology (infotech) companies on the healthcare scene presents both opportunities and challenges for the biopharma business model. To capitalize on new digital technologies from R&D through commercialization requires industry leaders to adopt new business models, develop new digital and data capabilities, and partner with innovators and payers worldwide. Written by two experts, both of whom have had decades of experience in the field, this book provides a comprehensive overview of the new business context and marketing models for biotech companies. Informed by extensive input by senior biotech executives and leading consultancies serving the industry, it analyzes the strategies and key success factors for the financing, development, and commercialization of novel therapeutic products, including strategies for engagement with patients, physicians and healthcare payers. Throughout case studies provide researchers, corporate marketers, senior managers, consultants, financial analysts, and other professionals involved in the biotech sector with insights, ideas, and models. JACQUALYN FOUSE, PhD, RETIRED PRESIDENT AND CHIEF OPERATING OFFICER, CELGENE “Biotech companies have long been innovators, using the latest technologies to enable cutting edge science to help patients with serious diseases. This book is essential to help biotech firms understand how they can–and must–apply the newest technologies including disruptive ones, alongside science, to innovate and bring new value to the healthcare system.” BRUCE DARROW, MD, PhD, CHIEF MEDICAL INFORMATION OFFICER, MOUNT SINAI HEALTH SYSTEM “Simon and Giovannetti have written an essential user’s manual explaining the complicated interplay of the patients who deserve cutting-edge medical care, the biotechnology companies (big and small) creating the breakthroughs, and the healthcare organizations and clinicians who bridge those worlds.” EMMANUEL BLIN, FORMER CHIEF STRATEGY OFFICER AND SENIOR VICE PRESIDENT, BRISTOL-MYERS SQUIBB “If you want to know where biopharma is going, read this book! Our industry is facing unprecedented opportunities driven by major scientific breakthroughs, while transforming itself to address accelerated landscape changes driven by digital revolutions and the emergence of value-based healthcare worldwide. In this ever-changing context, we all need to focus everything we do on the patients. They are why we exist as an industry, and this is ultimately what this insightful essay is really about.” JOHN MARAGANORE, PRESIDENT AND CHIEF EXECUTIVE OFFICER, ALNYLAM PHARMACEUTICALS “Since the mapping of the human genome was completed nearly 15 years ago, the biotechnology industry has led the rapid translation of raw science to today’s innovative medicines. However, the work does not stop in the lab. Delivering these novel medicines to patients is a complex and multifaceted process, which is elegantly described in this new book.” |
| data science in biotechnology: Animal Biotechnology National Research Council, Division on Earth and Life Studies, Board on Life Sciences, Board on Agriculture and Natural Resources, Committee on Agricultural Biotechnology, Health, and the Environment, Committee on Defining Science-Based Concerns Associated with Products of Animal Biotechnology, 2002-11-29 Genetic-based animal biotechnology has produced new food and pharmaceutical products and promises many more advances to benefit humankind. These exciting prospects are accompanied by considerable unease, however, about matters such as safety and ethics. This book identifies science-based and policy-related concerns about animal biotechnologyâ€key issues that must be resolved before the new breakthroughs can reach their potential. The book includes a short history of the field and provides understandable definitions of terms like cloning. Looking at technologies on the near horizon, the authors discuss what we know and what we fear about their effectsâ€the inadvertent release of dangerous microorganisms, the safety of products derived from biotechnology, the impact of genetically engineered animals on their environment. In addition to these concerns, the book explores animal welfare concerns, and our societal and institutional capacity to manage and regulate the technology and its products. This accessible volume will be important to everyone interested in the implications of the use of animal biotechnology. |
| data science in biotechnology: Data Science and Algorithms in Systems Radek Silhavy, Petr Silhavy, Zdenka Prokopova, 2023-01-03 This book offers real-world data science and algorithm design topics linked to systems and software engineering. Furthermore, articles describing unique techniques in data science, algorithm design, and systems and software engineering are featured. This book is the second part of the refereed proceedings of the 6th Computational Methods in Systems and Software 2022 (CoMeSySo 2022). The CoMeSySo 2022 conference, which is being hosted online, is breaking down barriers. CoMeSySo 2022 aims to provide a worldwide venue for debate of the most recent high-quality research findings. |
| data science in biotechnology: Data Processing Handbook for Complex Biological Data Sources Gauri Misra, 2019-03-23 Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. - Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level - Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data - Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing |
| data science in biotechnology: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science in biotechnology: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| data science in biotechnology: Data Science: New Issues, Challenges and Applications Gintautas Dzemyda, Jolita Bernatavičienė, Janusz Kacprzyk, 2020-02-13 This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others. |
| data science in biotechnology: Evolution of Translational Omics Institute of Medicine, Board on Health Sciences Policy, Board on Health Care Services, Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, 2012-09-13 Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials. |
| data science in biotechnology: Bioinformatics For Dummies Jean-Michel Claverie, Cedric Notredame, 2011-02-10 Were you always curious about biology but were afraid to sit through long hours of dense reading? Did you like the subject when you were in high school but had other plans after you graduated? Now you can explore the human genome and analyze DNA without ever leaving your desktop! Bioinformatics For Dummies is packed with valuable information that introduces you to this exciting new discipline. This easy-to-follow guide leads you step by step through every bioinformatics task that can be done over the Internet. Forget long equations, computer-geek gibberish, and installing bulky programs that slow down your computer. You’ll be amazed at all the things you can accomplish just by logging on and following these trusty directions. You get the tools you need to: Analyze all types of sequences Use all types of databases Work with DNA and protein sequences Conduct similarity searches Build a multiple sequence alignment Edit and publish alignments Visualize protein 3-D structures Construct phylogenetic trees This up-to-date second edition includes newly created and popular databases and Internet programs as well as multiple new genomes. It provides tips for using servers and places to seek resources to find out about what’s going on in the bioinformatics world. Bioinformatics For Dummies will show you how to get the most out of your PC and the right Web tools so you'll be searching databases and analyzing sequences like a pro! |
| data science in biotechnology: Recombinant DNA and Biotechnology Helen Kreuzer, Adrianne Massey, 2001 Written in clear, easy–to–understand language, this best–selling reference text and activities manual offers easy–to–implement lessons and classroom activities. Part I covers basic molecular biology, and Part II offers imaginative dry labs and wet labs that can be done by both college and precollege students. Part III is an innovative section addressing the social issues and public concerns of biotechnology. Extensive appendixes provide important background information on basic laboratory techniques and teaching resources, including overhead masters and templates. Adopted by numerous school systems, this unique book is an outgrowth of molecular biology and biotechnology teaching workshops. All of the exercises and lab activities have been extensively tested in the classroom by hundreds of high school teachers. Recombinant DNA and Biotechnology is designed to interest an international teaching audience and will enable all instructors to teach a reasonable amount of molecular biology and genetic engineering to students. No other book makes it so easy or compelling for teachers to incorporate the new biology into their biology, biological sciences, or general science curriculum. Recombinant DNA and Biotechnology: A Guide for Teachers will enable college and precollege teachers to plan and conduct an exciting and contemporary course on the basic principles, essential laboratory activities, and relevant social issues and concerns attendant to today′s molecular biology revolution. In addition to the complete text of the student edition, A Guide for Teachers also contains the answers to all discussion questions and extra background information and material on the scientific principles involved. |
| data science in biotechnology: Introduction to Bioinformatics Arthur M. Lesk, 2019 Lesk provides an accessible and thorough introduction to a subject which is becoming a fundamental part of biological science today. The text generates an understanding of the biological background of bioinformatics. |
| data science in biotechnology: Biotechnology in Functional Foods and Nutraceuticals Debasis Bagchi, Francis C. Lau, Dilip K. Ghosh, 2010-04-21 Modern food biotechnology is now a billion-dollar industry, producing functional foods and nutraceuticals that offer a whole host of increased health benefits, including prevention against illness, and chronic and degenerative conditions. Written by a team of top-tier researchers and scientists from around the world, Biotechnology in Functional Foo |
| data science in biotechnology: Biochemical Engineering and Biotechnology Ghasem Najafpour, 2015-02-24 Biochemical Engineering and Biotechnology, 2nd Edition, outlines the principles of biochemical processes and explains their use in the manufacturing of every day products. The author uses a diirect approach that should be very useful for students in following the concepts and practical applications. This book is unique in having many solved problems, case studies, examples and demonstrations of detailed experiments, with simple design equations and required calculations. - Covers major concepts of biochemical engineering and biotechnology, including applications in bioprocesses, fermentation technologies, enzymatic processes, and membrane separations, amongst others - Accessible to chemical engineering students who need to both learn, and apply, biological knowledge in engineering principals - Includes solved problems, examples, and demonstrations of detailed experiments with simple design equations and all required calculations - Offers many graphs that present actual experimental data, figures, and tables, along with explanations |
| data science in biotechnology: In Silico Dreams Brian S. Hilbush, 2021-07-28 Learn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and Biotechnology Will Create the Medicines of the Future delivers an illuminating and fresh perspective on the convergence of two powerful technologies: AI and biotech. Accomplished genomics expert, executive, and author Brian Hilbush offers readers a brilliant exploration of the most current work of pioneering tech giants and biotechnology startups who have already started disrupting healthcare. The book provides an in-depth understanding of the sources of innovation that are driving the shift in the pharmaceutical industry away from serendipitous therapeutic discovery and toward engineered medicines and curative therapies. In this fascinating book, you'll discover: An overview of the rise of data science methods and the paradigm shift in biology that led to the in silico revolution An outline of the fundamental breakthroughs in AI and deep learning and their applications across medicine A compelling argument for the notion that AI and biotechnology tools will rapidly accelerate the development of therapeutics A summary of innovative breakthroughs in biotechnology with a focus on gene editing and cell reprogramming technologies for therapeutic development A guide to the startup landscape in AI in medicine, revealing where investments are poised to shape the innovation base for the pharmaceutical industry Perfect for anyone with an interest in scientific topics and technology, In Silico Dreams also belongs on the bookshelves of decision-makers in a wide range of industries, including healthcare, technology, venture capital, and government. |
| data science in biotechnology: Healthcare Biotechnology Dimitris Dogramatzis, 2016-04-19 Foreseeing and planning for all of the possibilities and pitfalls involved in bringing a biotechnology innovation from inception to widespread therapeutic use takes strong managerial skills and a solid grounding in biopharmaceutical research and development procedures. Unfortunately there has been a dearth of resources for this aspect of the field. |
| data science in biotechnology: Medical Biotechnology Judit Pongracz, Mary Keen, 2009-01-01 Biotechnology encompasses the variety of methods available for manipulating living cells and organisms. It is having an increasing impact on all aspects of medicine, from helping in the understanding of the aetiology of disease, to its diagnosis and treatment. This growing importance of medical biotechnology means that a general understanding of this rapidly advancing field is essential for all medical graduates and medical scientists. This book places emphasis on the medical applications of biotechnology, rather than the details fo the experimental techniques--Back cover. |
| data science in biotechnology: Date Palm Biotechnology Shri Mohan Jain, Jameel M. Al-Khayri, Dennis V. Johnson, 2011-08-13 This important reference book is the first comprehensive resource worldwide that reflects research achievements in date palm biotechnology, documenting research events during the last four decades, current status, and future outlook. This book is essential for researchers, policy makers, and commercial entrepreneurs concerned with date palm. The book is invaluable for date palm biotechnology students and specialists. This monument is written by an international team of experienced researchers from both academia and industry. It consists of five sections covering all aspects of date palm biotechnology including A) Micropropagation, B) Somaclonal Variation, Mutation and Selection, C) Germplasm Biodiversity and Conservation, D) Genetics and Genetic Improvement, and E) Metabolites and Industrial Biotechnology. The book brings together the principles and practices of contemporary date palm biotechnology. Each chapter contains background knowledge related to the topic, followed by a comprehensive literature review of research methodology and results including the authors own experience including illustrative tables and photographs. |
| data science in biotechnology: Lattice Deepayan Sarkar, 2008-02-15 Written by the author of the lattice system, this book describes lattice in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to 150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics. |
| data science in biotechnology: Bioinformatics Algorithms Phillip Compeau, Pavel Pevzner, 1986-06 Bioinformatics Algorithms: an Active Learning Approach is one of the first textbooks to emerge from the recent Massive Online Open Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed online course (http://coursera.org/course/bioinformatics), this book presents students with a dynamic approach to learning bioinformatics. It strikes a unique balance between practical challenges in modern biology and fundamental algorithmic ideas, thus capturing the interest of students of biology and computer science students alike.Each chapter begins with a central biological question, such as Are There Fragile Regions in the Human Genome? or Which DNA Patterns Play the Role of Molecular Clocks? and then steadily develops the algorithmic sophistication required to answer this question. Hundreds of exercises are incorporated directly into the text as soon as they are needed; readers can test their knowledge through automated coding challenges on Rosalind (http://rosalind.info), an online platform for learning bioinformatics.The textbook website (http://bioinformaticsalgorithms.org) directs readers toward additional educational materials, including video lectures and PowerPoint slides. |
| data science in biotechnology: The Global Genome Eugene Thacker, 2006-09-08 How global biotechnology is redefining life itself. In the age of global biotechnology, DNA can exist as biological material in a test tube, as a sequence in a computer database, and as economically valuable information in a patent. In The Global Genome, Eugene Thacker asks us to consider the relationship of these three entities and argues that—by their existence and their interrelationships—they are fundamentally redefining the notion of biological life itself. Biological science and the biotech industry are increasingly organized at a global level, in large part because of the use of the Internet in exchanging biological data. International genome sequencing efforts, genomic databases, the development of World Intellectual Property policies, and the borderless business of biotech are all evidence of the global intersections of biology and informatics—of genetic codes and computer codes. Thacker points out the internal tension in the very concept of biotechnology: the products are more tech than bio, but the technology itself is fully biological, composed of the biomaterial labor of genes, proteins, cells, and tissues. Is biotechnology a technology at all, he asks, or is it a notion of life itself that is inseparable from its use in the biotech industry? The three sections of the book cover the three primary activities of biotechnology today: the encoding of biological materials into digital form—as in bioinformatics and genomics; its recoding in various ways—including the biocolonialism of mapping genetically isolated ethnic populations and the newly pervasive concern over biological security; and its decoding back into biological materiality—as in tissue engineering and regenerative medicine. Thacker moves easily from science to philosophy to political economics, enlivening his account with ideas from such thinkers as Georges Bataille, Georges Canguilhem, Michel Foucault, Antonio Negri, and Paul Virilio. The global genome, says Thacker, makes it impossible to consider biotechnology without the context of globalism. |
| data science in biotechnology: Making PCR Paul Rabinow, 2011-11-27 Making PCR is the fascinating, behind-the-scenes account of the invention of one of the most significant biotech discoveries in our time—the polymerase chain reaction. Transforming the practice and potential of molecular biology, PCR extends scientists' ability to identify and manipulate genetic materials and accurately reproduces millions of copies of a given segment in a short period of time. It makes abundant what was once scarce—the genetic material required for experimentation. Making PCR explores the culture of biotechnology as it emerged at Certus Corporation during the 1980s and focuses on its distinctive configuration of scientific, technical, social, economic, political, and legal elements, each of which had its own separate trajectory over the preceding decade. The book contains interviews with the remarkable cast of characters who made PCR, including Kary Mullin, the maverick who received the Nobel prize for discovering it, as well as the team of young scientists and the company's business leaders. This book shows how a contingently assembled practice emerged, composed of distinctive subjects, the site where they worked, and the object they invented. Paul Rabinow paints a . . . picture of the process of discovery in Making PCR: A Story of Biotechnology [and] teases out every possible detail. . . . Makes for an intriguing read that raises many questions about our understanding of the twisting process of discovery itself.—David Bradley, New Scientist Rabinow's book belongs to a burgeoning genre: ethnographic studies of what scientists actually do in the lab. . . . A bold move.—Daniel Zalewski, Lingua Franca [Making PCR is] exotic territory, biomedical research, explored. . . . Rabinow describes a dance: the immigration and repatriation of scientists to and from the academic and business worlds.—Nancy Maull, New York Times Book Review |
| data science in biotechnology: Data Science Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, 2022-08-15 This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science. Key Features • Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science. • Presents predictive outcomes by applying data science techniques to real-life applications. • Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. • Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful. |
| data science in biotechnology: Intelligent Computing and Innovation on Data Science Sheng-Lung Peng, Le Hoang Son, G. Suseendran, D. Balaganesh, 2020-05-14 This book covers both basic and high-level concepts relating to the intelligent computing paradigm and data sciences in the context of distributed computing, big data, data sciences, high-performance computing and Internet of Things. It is becoming increasingly important to develop adaptive, intelligent computing-centric, energy-aware, secure and privacy-aware systems in high-performance computing and IoT applications. In this context, the book serves as a useful guide for industry practitioners, and also offers beginners a comprehensive introduction to basic and advanced areas of intelligent computing. Further, it provides a platform for researchers, engineers, academics and industrial professionals around the globe to showcase their recent research concerning recent trends. Presenting novel ideas and stimulating interesting discussions, the book appeals to researchers and practitioners working in the field of information technology and computer science. |
| data science in biotechnology: Statistical Methods in Bioinformatics Warren J. Ewens, Gregory R. Grant, 2005-09-30 Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community. This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods. The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized. The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text. Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science. Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999. Comments on the first edition: This book would be an ideal text for a postgraduate course...[and] is equally well suited to individual study.... I would recommend the book highly. (Biometrics) Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces. (Naturwissenschaften) The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details. (Journal American Statistical Association) The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book. (Metrika) |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
