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Author: Colby T. Ford
Publisher: "O'Reilly Media, Inc."
ISBN: 1098139003
Category : Computers
Languages : en
Pages : 319
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Book Description
This practical guide bridges the gap between general cloud computing architecture in Microsoft Azure and scientific computing for bioinformatics and genomics. You'll get a solid understanding of the architecture patterns and services that are offered in Azure and how they might be used in your bioinformatics practice. You'll get code examples that you can reuse for your specific needs. And you'll get plenty of concrete examples to illustrate how a given service is used in a bioinformatics context. You'll also get valuable advice on how to: Use enterprise platform services to easily scale your bioinformatics workloads Organize, query, and analyze genomic data at scale Build a genomics data lake and accompanying data warehouse Use Azure Machine Learning to scale your model training, track model performance, and deploy winning models Orchestrate and automate processing pipelines using Azure Data Factory and Databricks Cloudify your organization's existing bioinformatics pipelines by moving your workflows to Azure high-performance compute services And more
Author: Colby T. Ford
Publisher: "O'Reilly Media, Inc."
ISBN: 1098139003
Category : Computers
Languages : en
Pages : 319
Get Book Here
Book Description
This practical guide bridges the gap between general cloud computing architecture in Microsoft Azure and scientific computing for bioinformatics and genomics. You'll get a solid understanding of the architecture patterns and services that are offered in Azure and how they might be used in your bioinformatics practice. You'll get code examples that you can reuse for your specific needs. And you'll get plenty of concrete examples to illustrate how a given service is used in a bioinformatics context. You'll also get valuable advice on how to: Use enterprise platform services to easily scale your bioinformatics workloads Organize, query, and analyze genomic data at scale Build a genomics data lake and accompanying data warehouse Use Azure Machine Learning to scale your model training, track model performance, and deploy winning models Orchestrate and automate processing pipelines using Azure Data Factory and Databricks Cloudify your organization's existing bioinformatics pipelines by moving your workflows to Azure high-performance compute services And more
Author: Geraldine A. Van der Auwera
Publisher: O'Reilly Media
ISBN: 1491975164
Category : Science
Languages : en
Pages : 496
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Book Description
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
Author: Catherine Vacher
Publisher: Wiley
ISBN: 9781119573371
Category : Computers
Languages : en
Pages : 0
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Book Description
Perform genome analysis and sequencing of data with Amazon Web Services Genomics in the AWS Cloud: Analyzing Genetic Code Using Amazon Web Services enables a person who has moderate familiarity with AWS Cloud to perform full genome analysis and research. Using the information in this book, you’ll be able to take a FASTQ file containing raw data from a lab or a BAM file from a service provider and perform genome analysis on it. You’ll also be able to identify potentially pathogenic gene sequences. • Get an introduction to Whole Genome Sequencing (WGS) • Make sense of WGS on AWS • Master AWS services for genome analysis Some key advantages of using AWS for genomic analysis is to help researchers utilize a wide choice of compute services that can process diverse datasets in analysis pipelines. Genomic sequencers that generate raw data files are located in labs on premises and AWS provides solutions to make it easy for customers to transfer these files to AWS reliably and securely. Storing Genomics and Medical (e.g., imaging) data at different stages requires enormous storage in a cost-effective manner. Amazon Simple Storage Service (Amazon S3), Amazon Glacier, and Amazon Elastics Block Store (Amazon EBS) provide the necessary solutions to securely store, manage, and scale genomic file storage. Moreover, the storage services can interface with various compute services from AWS to process these files. Whether you’re just getting started or have already been analyzing genomics data using the AWS Cloud, this book provides you with the information you need in order to use AWS services and features in the ways that will make the most sense for your genomic research.
Author: Jonah Carrio Andersson
Publisher: "O'Reilly Media, Inc."
ISBN: 1098113292
Category : Computers
Languages : en
Pages : 478
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Book Description
If your organization plans to modernize services and move to the cloud from legacy software or a private cloud on premises, this book is for you. Software developers, solution architects, cloud engineers, and anybody interested in cloud technologies will learn fundamental concepts for cloud computing, migration, transformation, and development using Microsoft Azure. Author and Microsoft MVP Jonah Carrio Andersson guides you through cloud computing concepts and deployment models, the wide range of modern cloud technologies, application development with Azure, team collaboration services, security services, and cloud migration options in Microsoft Azure. You'll gain insight into the Microsoft Azure cloud services that you can apply in different business use cases, software development projects, and modern solutions in the cloud. You'll also become fluent with Azure cloud migration services, serverless computing technologies that help your development team work productively, Azure IoT, and Azure cognitive services that make your application smarter. This book also provides real-world advice and best practices based on the author's own Azure migration experience. Gain insight into which Azure cloud service best suits your company's particular needs Understand how to use Azure for different use cases and specific technical requirements Start developing cloud services, applications, and solutions in the Azure environment Learn how to migrate existing legacy applications to Microsoft Azure
Author: Diego A. Forero
Publisher: CRC Press
ISBN: 1000405680
Category : Science
Languages : en
Pages : 509
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Book Description
Advances in high-throughput biological methods have led to the publication of a large number of genome-wide studies in human and animal models. In this context, recent tools from bioinformatics and computational biology have been fundamental for the analysis of these genomic studies. The book Bioinformatics and Human Genomics Research provides updated and comprehensive information about multiple approaches of the application of bioinformatic tools to research in human genomics. It covers strategies analysis of genome-wide association studies, genome-wide expression studies and genome-wide DNA methylation, among other topics. It provides interesting strategies for data mining in human genomics, network analysis, prediction of binding sites for miRNAs and transcription factors, among other themes. Experts from all around the world in bioinformatics and human genomics have contributed chapters in this book. Readers will find this book as quite useful for their in silico explorations, which would contribute to a better and deeper understanding of multiple biological processes and of pathophysiology of many human diseases.
Author: Upendra Kumar Devisetty
Publisher: Packt Publishing Ltd
ISBN: 1804613010
Category : Computers
Languages : en
Pages : 270
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Book Description
Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key FeaturesApply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learnDiscover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is for This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.
Author: Ibrokhim Y. Abdurakhmonov
Publisher: BoD – Books on Demand
ISBN: 1789232686
Category : Medical
Languages : en
Pages : 190
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Book Description
Bioinformatics has evolved significantly in the era of post genomics and big data. Huge advancements were made toward storing, handling, mining, comparing, extracting, clustering and analysis as well as visualization of big macromolecular data using novel computational approaches, machine and deep learning methods, and web-based server tools. There are extensively ongoing world-wide efforts to build the resources for regional hosting, organized and structured access and improving the pre-existing bioinformatics tools to efficiently and meaningfully analyze day-to-day increasing big data. This book intends to provide the reader with updates and progress on genomic data analysis, data modeling and network-based system tools.
Author: Rong Kun Jason Tan
Publisher: Springer
ISBN: 3319732145
Category : Technology & Engineering
Languages : en
Pages : 106
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Book Description
This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
Author: Abhirup Khanna
Publisher: John Wiley & Sons
ISBN: 1394234171
Category : Computers
Languages : en
Pages : 737
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Book Description
The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.
Author: Hassan, Ahdi
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 372
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Book Description
The Healthcare sector is experiencing a mindset change with the advent of Healthcare 5.0, bringing forth improved patient care and system efficiency. However, this transformation poses significant challenges. The growing digitization of healthcare systems raises concerns about the security and privacy of patient data, making seamless data sharing and collaboration increasingly complex tasks. Additionally, as the volume of healthcare data expands exponentially, efficient handling and analysis become vital for optimizing healthcare delivery and patient outcomes. Addressing these multifaceted issues is crucial for healthcare professionals, IT experts, data scientists, and researchers seeking to fully harness the potential of Healthcare 5.0. Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security presents a comprehensive solution to the pressing challenges in the digitalized healthcare industry. This research book dives into the principles of Healthcare 5.0 and explores practical implementation through cloud computing, data analytics, and federated learning. Readers will gain profound insights into the role of cloud computing in managing vast amounts of healthcare data, such as electronic health records and real-time analytics. Cloud-based frameworks, architectures, and relevant use cases are explored to optimize healthcare delivery and improve patient outcomes.
