Applications Deep Learning Genomics (32 results)

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Hardback. Condition: New. The objective of the book is to use advanced deep learning techniques to unlock the complexities of genomic data. It reveals hidden and unknown patterns, to improve our understanding the role of genetics in diseases, speeding up drug discovery processes, illuminating evolutionary trajectories, and dealing with the challenges posed by large genomic datasets. It also addresses ethical concerns, provides real-world applications, reviews future frontiers like as quantum computing and multi-omics integration, and presents a thorough picture of the genetic environment. Overall, the book aims to enhance our ability to use genomics for more precise forecasts, personalised therapies, and a better understanding of the underlying genetic fabric that defines life.…

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Hardback. Condition: New. The objective of the book is to use advanced deep learning techniques to unlock the complexities of genomic data. It reveals hidden and unknown patterns, to improve our understanding the role of genetics in diseases, speeding up drug discovery processes, illuminating evolutionary trajectories, and dealing with the challenges posed by large genomic datasets. It also addresses ethical concerns, provides real-world applications, reviews future frontiers like as quantum computing and multi-omics integration, and presents a thorough picture of the genetic environment. Overall, the book aims to enhance our ability to use genomics for more precise forecasts, personalised therapies, and a better understanding of the underlying genetic fabric that defines life.…

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Features:Apply deep learning algorithms to solve real-world problems in the field of genomics Extract biological insights from deep learning models built from genomic datasets Train, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomics Book 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 Learn:Discover the machine learning applications for genomics Explore deep learning concepts and methodologies for genomics applications Understand supervised deep learning algorithms for genomics applications Get to grips with unsupervised deep learning with autoencoders Improve deep learning models using generative models Operationalize deep learning models from genomics datasets Visualize and interpret deep learning models Understand deep learning challenges, pitfalls, and best practices Who 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.…

Taschenbuch. Condition: Neu. Deep Learning for Genomics | Data-driven approaches for genomics applications in life sciences and biotechnology | Upendra Kumar Devisetty | Taschenbuch | Englisch | 2022 | Packt Publishing | EAN 9781804615447 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Hardcover. Condition: new. Hardcover. The objective of the book is to use advanced deep learning techniques to unlock the complexities of genomic data. It reveals hidden and unknown patterns, to improve our understanding the role of genetics in diseases, speeding up drug discovery processes, illuminating evolutionary trajectories, and dealing with the challenges posed by large genomic datasets. It also addresses ethical concerns, provides real-world applications, reviews future frontiers like as quantum computing and multi-omics integration, and presents a thorough picture of the genetic environment. Overall, the book aims to enhance our ability to use genomics for more precise forecasts, personalised therapies, and a better understanding of the underlying genetic fabric that defines life. The book investigates future frontiers such as quantum computing and multi-omics integration, and presents a picture of the genetic environment to advance enhanced genomics, enabling more precise forecasts, and personalised therapies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

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