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Author: Isabel Moreno Indias
Publisher: Frontiers Media SA
Published: 2022-08-02
Total Pages: 133
ISBN-13: 2889766780
DOWNLOAD EBOOKDownload or read book Microbiome and Machine Learning written by Isabel Moreno Indias and published by Frontiers Media SA. This book was released on 2022-08-02 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bioinformatics and Machine Learning in Human Microbiome Analysis written by Kuncheng Song and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author: Shailza Singh
Publisher: Springer Nature
Published: 2020-12-07
Total Pages: 207
ISBN-13: 9811585628
DOWNLOAD EBOOKThe 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.
Download or read book Metagenomic Systems Biology written by Shailza Singh and published by Springer Nature. This book was released on 2020-12-07 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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: Matthew W. Chang
Publisher: John Wiley & Sons
Published: 2022-05-03
Total Pages: 340
ISBN-13: 3527825487
DOWNLOAD EBOOKPrinciples in Microbiome Engineering Provides an overview of the techniques and applications insight into the complex composition and interactions of microbiomes Microbiomes, the communities of microorganisms that inhabit specific ecosystems or organisms, can be engineered to modify the structure of microbiota and reestablish ecological balance. In recent years, a better understanding of microbial composition and host-microbe interactions has led to the development of new applications for improving human health and increasing agricultural productivity and quality. Principles in Microbiome Engineering introduces readers to the tools and applications involved in manipulating the composition of a microbial community to improve the function of an eco-system. Covering a range of key topics, this up-to-date volume discusses current research in areas such as microbiome-based therapeutics for human diseases, crop plant breeding, animal husbandry, soil engineering, food and beverage applications, and more. Divided into three sections, the text first describes the critical roles of systems biology, synthetic biology, computer modelling, and machine learning in microbiome engineering. Next, the volume explores various state-of-the-art applications, including cancer immunotherapy and prevention of diseases associated with the human microbiome, followed by a concluding section offering perspectives on the future of microbiome engineering and potential applications. Introduces a variety of applications of microbiome engineering in the fields of medicine, agriculture, and food and beverage products Presents current research into the complex interactions and relationships between microbiomes and biotic and abiotic elements of their environments Examines the use of technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics to advance understanding of microbiomes Discusses the engineering of microbiomes to address human health conditions such as neuro psychiatric disorders and autoimmune and inflammatory diseases Edited and authored by leading researchers in the rapidly evolving field, Principles in Microbiome Engineering is an essential resource for biotechnologists, biochemists, microbiologists, pharmacologists, and practitioners working in the biotechnology and pharmaceutical industries.
Download or read book Principles in Microbiome Engineering written by Matthew W. Chang and published by John Wiley & Sons. This book was released on 2022-05-03 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles in Microbiome Engineering Provides an overview of the techniques and applications insight into the complex composition and interactions of microbiomes Microbiomes, the communities of microorganisms that inhabit specific ecosystems or organisms, can be engineered to modify the structure of microbiota and reestablish ecological balance. In recent years, a better understanding of microbial composition and host-microbe interactions has led to the development of new applications for improving human health and increasing agricultural productivity and quality. Principles in Microbiome Engineering introduces readers to the tools and applications involved in manipulating the composition of a microbial community to improve the function of an eco-system. Covering a range of key topics, this up-to-date volume discusses current research in areas such as microbiome-based therapeutics for human diseases, crop plant breeding, animal husbandry, soil engineering, food and beverage applications, and more. Divided into three sections, the text first describes the critical roles of systems biology, synthetic biology, computer modelling, and machine learning in microbiome engineering. Next, the volume explores various state-of-the-art applications, including cancer immunotherapy and prevention of diseases associated with the human microbiome, followed by a concluding section offering perspectives on the future of microbiome engineering and potential applications. Introduces a variety of applications of microbiome engineering in the fields of medicine, agriculture, and food and beverage products Presents current research into the complex interactions and relationships between microbiomes and biotic and abiotic elements of their environments Examines the use of technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics to advance understanding of microbiomes Discusses the engineering of microbiomes to address human health conditions such as neuro psychiatric disorders and autoimmune and inflammatory diseases Edited and authored by leading researchers in the rapidly evolving field, Principles in Microbiome Engineering is an essential resource for biotechnologists, biochemists, microbiologists, pharmacologists, and practitioners working in the biotechnology and pharmaceutical industries.
Author: Michael Wiest
Publisher:
Published: 2019
Total Pages: 31
ISBN-13:
DOWNLOAD EBOOKThe human microbiota exhibit a highly dynamic composition over the course of life and changes in the human gut microbiota have been associated with human health or disease. Reprogramming of the gut microbiota by interventions that counter these changes and promote long-lasting health has been an emerging topic in microbiome research. Predicting changes in the gut microbiome is therefore crucial for the nature and design of these interventions. Here, we report on a new method based on deep learning to forecast changes in the microbiome. We processed and analyzed nine time-course datasets of the human gut microbiome, identifying the main microorganisms present in these microbial communities at any given time. We then used an encoder-decoder neural network to train a model that successfully predicts the progression of the microbiome composition over time given only five time points of context data. Our results demonstrate the ability to predict the fate of the human gut microbiome into the future, providing the foundation for rational intervention design.
Download or read book Predicting the Complexity and Progression of the Gut Microbiome Using Temporal Data and Deep Learning written by Michael Wiest and published by . This book was released on 2019 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human microbiota exhibit a highly dynamic composition over the course of life and changes in the human gut microbiota have been associated with human health or disease. Reprogramming of the gut microbiota by interventions that counter these changes and promote long-lasting health has been an emerging topic in microbiome research. Predicting changes in the gut microbiome is therefore crucial for the nature and design of these interventions. Here, we report on a new method based on deep learning to forecast changes in the microbiome. We processed and analyzed nine time-course datasets of the human gut microbiome, identifying the main microorganisms present in these microbial communities at any given time. We then used an encoder-decoder neural network to train a model that successfully predicts the progression of the microbiome composition over time given only five time points of context data. Our results demonstrate the ability to predict the fate of the human gut microbiome into the future, providing the foundation for rational intervention design.
Author: K.P. Soman
Publisher: PHI Learning Pvt. Ltd.
Published: 2009-02-02
Total Pages: 495
ISBN-13: 8120334353
DOWNLOAD EBOOKSupport 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.
Download or read book Machine Learning with SVM and Other Kernel Methods written by K.P. Soman and published by PHI Learning Pvt. Ltd.. This book was released on 2009-02-02 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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: Nikhil Ketkar
Publisher: Apress
Published: 2021-04-10
Total Pages: 306
ISBN-13: 9781484253632
DOWNLOAD EBOOKMaster 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.
Download or read book Deep Learning with Python written by Nikhil Ketkar and published by Apress. This book was released on 2021-04-10 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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: Yuqing Zhong
Publisher:
Published: 2017
Total Pages: 62
ISBN-13:
DOWNLOAD EBOOKObesity is a common disease among all ages that has threatened human health and has become a global concern. Gut microbiota can affect human metabolism and thus may modulate obesity. Certain mixes of gut microbiota can protect the host to be healthy or predispose the host to obesity. Modern next-generation sequencing technique allows accessing huge amount of genetic information underlying microbiota and thus provides new insights into the functionality of these micro-organisms and their interactions with the host. Multiple previous studies have demonstrated that the microbiome might contribute to obesity by increasing dietary energy harvest, promoting fat deposition and triggering systemic inflammation. However, these researches are either based on lab cultivation studies or basic statistical analysis. In order to further explore how gut microbiota affect obesity, this thesis utilize a series of machine learning methods to analyze large amount of metagenomics data from human gut microbiome. The publicly available HMP (Human Microbiome Project) metagenomic sequencing data, contain microbiome data for healthy adults, including overweight and obese individuals, were used for this study. HMP gut data were organized based on two different feature definitions: taxonomic information and metabolic reconstruction information. Several widely used classification algorithms: namely Naive Bayes, Random Forest, SVM and elastic net logistic regression were applied to predict healthy or obese status of the subjects based on the cross-validation accuracy. Furthermore, the corresponding feature selection algorithms were used to identify signature features in each dataset that lead to the differences between healthy and obese samples. The results showed that these algorithms perform poorly on taxonomic data than metabolic pathway data though lots of selected taxa are still supported by literature. Among all the combinations between different algorithms and data, elastic net logistic regression has the best cross-validation performance and thus becomes the best model. In this model, several important features are found and some of these are consistent with the previous studies. Rerunning classifiers by using features selected by elastic net logistic regression again further improved the performance of the classifiers. On the other hand, this study uncovered some new features that haven't been supported by previous studies. The new features could also be the potential target to distinguish obese and healthy subjects. The present thesis work compares the strengths and weaknesses of different machine learning techniques with different types of features originating from the same metagenomics data. The features selected by these models could provide a deep understanding of the metabolic mechanisms of micro-organisms. It is therefore worth to comprehensively understand the differences of gut microbiota between healthy and obese subjects, and particularly how gut microbiome affects obesity.
Download or read book Investigating Human Gut Microbiome in Obesity with Machine Learning Methods written by Yuqing Zhong and published by . This book was released on 2017 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obesity is a common disease among all ages that has threatened human health and has become a global concern. Gut microbiota can affect human metabolism and thus may modulate obesity. Certain mixes of gut microbiota can protect the host to be healthy or predispose the host to obesity. Modern next-generation sequencing technique allows accessing huge amount of genetic information underlying microbiota and thus provides new insights into the functionality of these micro-organisms and their interactions with the host. Multiple previous studies have demonstrated that the microbiome might contribute to obesity by increasing dietary energy harvest, promoting fat deposition and triggering systemic inflammation. However, these researches are either based on lab cultivation studies or basic statistical analysis. In order to further explore how gut microbiota affect obesity, this thesis utilize a series of machine learning methods to analyze large amount of metagenomics data from human gut microbiome. The publicly available HMP (Human Microbiome Project) metagenomic sequencing data, contain microbiome data for healthy adults, including overweight and obese individuals, were used for this study. HMP gut data were organized based on two different feature definitions: taxonomic information and metabolic reconstruction information. Several widely used classification algorithms: namely Naive Bayes, Random Forest, SVM and elastic net logistic regression were applied to predict healthy or obese status of the subjects based on the cross-validation accuracy. Furthermore, the corresponding feature selection algorithms were used to identify signature features in each dataset that lead to the differences between healthy and obese samples. The results showed that these algorithms perform poorly on taxonomic data than metabolic pathway data though lots of selected taxa are still supported by literature. Among all the combinations between different algorithms and data, elastic net logistic regression has the best cross-validation performance and thus becomes the best model. In this model, several important features are found and some of these are consistent with the previous studies. Rerunning classifiers by using features selected by elastic net logistic regression again further improved the performance of the classifiers. On the other hand, this study uncovered some new features that haven't been supported by previous studies. The new features could also be the potential target to distinguish obese and healthy subjects. The present thesis work compares the strengths and weaknesses of different machine learning techniques with different types of features originating from the same metagenomics data. The features selected by these models could provide a deep understanding of the metabolic mechanisms of micro-organisms. It is therefore worth to comprehensively understand the differences of gut microbiota between healthy and obese subjects, and particularly how gut microbiome affects obesity.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Published: 2018-03-01
Total Pages: 123
ISBN-13: 0309468698
DOWNLOAD EBOOKA 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.
Download or read book Environmental Chemicals, the Human Microbiome, and Health Risk written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-03-01 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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: Somnath Datta
Publisher: Springer Nature
Published: 2021-10-27
Total Pages: 349
ISBN-13: 3030733513
DOWNLOAD EBOOKMicrobiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
Download or read book Statistical Analysis of Microbiome Data written by Somnath Datta and published by Springer Nature. This book was released on 2021-10-27 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
