Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Metagenomic Systems Biology PDF full book. Access full book title Metagenomic Systems Biology by Shailza Singh. Download full books in PDF and EPUB format.
Author: Shailza Singh Publisher: Springer Nature ISBN: 9811585628 Category : Science Languages : en Pages : 207
Book Description
The book serves as an amalgamation of knowledge and principles used in the area of systems and synthetic biology, and targets inter-disciplinary research groups. The readers from diversified areas would be benefited by the valuable resources and information available in one book. Microbiome projects with efficient data handling can fuel progress in the area of microbial synthetic biology by providing a ready to use plug and play chassis. Advances in gene editing technology such as the use of tailor made synthetic transcription factors will further enhance the availability of synthetic devices to be applied in the fields of environment, agriculture and health. The different chapters of the book reviews a broad range of topics, including food microbiome in ecology, use of microbiome in personalized medicine, machine learning in biomedicine. The book also describes ways to harness and exploit the incredible amounts of genomic data. The book is not only limited to medicine but also caters to the needs of environmentalists, biochemical engineers etc. It will be of interest to advanced students and researchers in life sciences, computational biology, microbiology and other inter-disciplinary areas.
Author: Shailza Singh Publisher: Springer Nature ISBN: 9811585628 Category : Science Languages : en Pages : 207
Book Description
The book serves as an amalgamation of knowledge and principles used in the area of systems and synthetic biology, and targets inter-disciplinary research groups. The readers from diversified areas would be benefited by the valuable resources and information available in one book. Microbiome projects with efficient data handling can fuel progress in the area of microbial synthetic biology by providing a ready to use plug and play chassis. Advances in gene editing technology such as the use of tailor made synthetic transcription factors will further enhance the availability of synthetic devices to be applied in the fields of environment, agriculture and health. The different chapters of the book reviews a broad range of topics, including food microbiome in ecology, use of microbiome in personalized medicine, machine learning in biomedicine. The book also describes ways to harness and exploit the incredible amounts of genomic data. The book is not only limited to medicine but also caters to the needs of environmentalists, biochemical engineers etc. It will be of interest to advanced students and researchers in life sciences, computational biology, microbiology and other inter-disciplinary areas.
Author: Erik Bongcam-Rudloff Publisher: Frontiers Media SA ISBN: 2832556035 Category : Science Languages : en Pages : 209
Book Description
Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.
Author: K.P. Soman Publisher: PHI Learning Pvt. Ltd. ISBN: 8120334353 Category : Computers Languages : en Pages : 495
Book Description
Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to solve any pattern classification problems with ease and that too in Excel. KEY FEATURES Extensive coverage of Lagrangian duality and iterative methods for optimization Separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing A chapter on latest sequential minimization algorithms and its modifications to do online learning Step-by-step method of solving the SVM based classification problem in Excel. Kernel versions of PCA, CCA and ICA The CD accompanying the book includes animations on solving SVM training problem in Microsoft EXCEL and by using SVMLight software . In addition, Matlab codes are given for all the formulations of SVM along with the data sets mentioned in the exercise section of each chapter.
Author: Trevor C. Charles Publisher: Springer ISBN: 3319615106 Category : Science Languages : en Pages : 256
Book Description
In this book, the latest tools available for functional metagenomics research are described.This research enables scientists to directly access the genomes from diverse microbial genomes at one time and study these “metagenomes”. Using the modern tools of genome sequencing and cloning, researchers have now been able to harness this astounding metagenomic diversity to understand and exploit the diverse functions of microorganisms. Leading scientists from around the world demonstrate how these approaches have been applied in many different settings, including aquatic and terrestrial habitats, microbiomes, and many more environments. This is a highly informative and carefully presented book, providing microbiologists with a summary of the latest functional metagenomics literature on all specific habitats.
Author: National Academies of Sciences, Engineering, and Medicine Publisher: National Academies Press ISBN: 0309468698 Category : Science Languages : en Pages : 123
Book Description
A great number of diverse microorganisms inhabit the human body and are collectively referred to as the human microbiome. Until recently, the role of the human microbiome in maintaining human health was not fully appreciated. Today, however, research is beginning to elucidate associations between perturbations in the human microbiome and human disease and the factors that might be responsible for the perturbations. Studies have indicated that the human microbiome could be affected by environmental chemicals or could modulate exposure to environmental chemicals. Environmental Chemicals, the Human Microbiome, and Health Risk presents a research strategy to improve our understanding of the interactions between environmental chemicals and the human microbiome and the implications of those interactions for human health risk. This report identifies barriers to such research and opportunities for collaboration, highlights key aspects of the human microbiome and its relation to health, describes potential interactions between environmental chemicals and the human microbiome, reviews the risk-assessment framework and reasons for incorporating chemicalâ€"microbiome interactions.
Author: Gang Ye Publisher: Frontiers Media SA ISBN: 283254956X Category : Science Languages : en Pages : 162
Book Description
The pathogenic microbiome is the community of microorganisms that live in humans or animals and cause disease. These microorganisms include bacteria, viruses, fungi, protozoa, etc. They usually live in the host's skin, mouth, intestinal tract, genitourinary tract, etc. Normally, there is a state of equilibrium between the host and these microorganisms, but when this equilibrium is disturbed, these microorganisms become the pathogenic microbiome and cause disease. To advance the field of microbiome research, artificial intelligence methods, especially machine learning and deep learning, have recently been used as important tools due to their powerful predictive and informative potential. Classical machine learning algorithms such as linear regression, random forests, support vector machines, etc. perform well on microbiome data. However, as algorithms have been iteratively updated, these models have long been relegated to the basics. Linear regression models are now more often used to interpret these models more intuitively by using the output of other models as input. Deep learning is a branch of machine learning that involves a large number of neural network structures. Deep learning relies on neurons whose role is to transform the input and propagate it forward to the next neuron. Deep learning is currently being used with spectacular success in areas such as image recognition, text processing and automatic translation. As a result, a growing number of researchers are attempting to apply deep learning techniques to biomedical data analysis. Although there are still challenges in practical applications, such as model interpretability, data availability, model evaluation and selection, machine learning and deep learning are very promising tools in pathogenic microbiome research. This Research Topic, therefore, aims to contribute to the latest advances in machine learning, especially deep learning, and to explore new applications of related techniques in pathogenic microbiome research, trying to find relationships between microbiome and human health as well as the environment by studying high-throughput sequencing data of microbes, laying the foundation for further applications for subsequent treatment or forensic identification. We welcome submissions of Original Research, Brief Research Report, Review, Mini-Review, Methods, Perspective and Opinion articles that focus on, but are not limited to, the utilization of machine learning and deep learning to address the following subtopics. 1. Classification and identification of pathogenic microorganisms 2. Virulence prediction of pathogenic microorganisms 3. Antimicrobial resistance prediction of pathogenic microorganisms 4. Population structure and epidemiology of pathogenic microorganisms-related diseases 5. Immunological studies of pathogenic microorganisms 6. Drug target prediction for pathogenic microorganisms-related diseases
Author: Nikhil Ketkar Publisher: Apress ISBN: 9781484253632 Category : Computers Languages : en Pages : 306
Book Description
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
Author: Food Forum Publisher: National Academies Press ISBN: 030926586X Category : Medical Languages : en Pages : 197
Book Description
The Food Forum convened a public workshop on February 22-23, 2012, to explore current and emerging knowledge of the human microbiome, its role in human health, its interaction with the diet, and the translation of new research findings into tools and products that improve the nutritional quality of the food supply. The Human Microbiome, Diet, and Health: Workshop Summary summarizes the presentations and discussions that took place during the workshop. Over the two day workshop, several themes covered included: The microbiome is integral to human physiology, health, and disease. The microbiome is arguably the most intimate connection that humans have with their external environment, mostly through diet. Given the emerging nature of research on the microbiome, some important methodology issues might still have to be resolved with respect to undersampling and a lack of causal and mechanistic studies. Dietary interventions intended to have an impact on host biology via their impact on the microbiome are being developed, and the market for these products is seeing tremendous success. However, the current regulatory framework poses challenges to industry interest and investment.
Author: Alessio Fasano Publisher: MIT Press ISBN: 0262044277 Category : Science Languages : en Pages : 551
Book Description
Why the microbiome--our rich inner ecosystem of microorganisms--may hold the keys to human health. We are at the dawn of a new scientific revolution. Our understanding of how to treat and prevent diseases has been transformed by knowledge of the microbiome--the rich ecosystem of microorganisms that is in and on every human. These microbial hitchhikers may hold the keys to human health. In Gut Feelings, Alessio Fasano and Susie Flaherty show why we must go beyond the older, myopic view of microorganisms as our enemies to a broader understanding of the microbiome as a parallel civilization that we need to understand, respect, and engage with for the benefit of our own health.
Author: Shailza Singh
Author: Shailza Singh
Author: Erik Bongcam-Rudloff
Author: K.P. Soman
Author: Isabel Moreno Indias
Author: Trevor C. Charles
Author: National Academies of Sciences, Engineering, and Medicine
Author: Gang Ye
Author: Nikhil Ketkar
Author: Food Forum
Author: Alessio Fasano