Applications of Metagenomics: Agriculture, Environment, and Health examines current metagenomics methods and their applications in soil, polluted environment sites, agriculture production, and health care, with separate sections dedicated to each application area. Special attention is paid to the biotechnological study of novel microbial resources for social welfare. Beyond applications, the book discusses evolving next generation technology and techniques used for carrying out metagenomics studies, and in doing so highlights the latest research and advances in the field, along with ways to adapt these approaches for different study types across the biological sciences.Chapter topics range from metagenomics for studying root microbial communities to microbial diversity of the rhizosphere, fungal diversity, microbial biodiversity in forest environments, the human microbiome, and disease epidemiology, with one chapter dedicated to Covid-19 metagenomics.

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 DescriptionDeep 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 forThis 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.