Xia yinglin (83 resultados)

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Hardcover. Condición: new. Hardcover. Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field.Presents a thorough overview of machine learning algorithms for microbiome statistics.Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models.Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering.Investigates and applies various cross-validation techniques step-by-step.Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data.Offers all related R codes and the datasets from the authors first-hand microbiome research and publicly available data. This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Hardcover. Condición: new. Hardcover. This unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing. It includes real-world data from the authors research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter so that these new methods can be readily applied in their own research. Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Taschenbuch. Condición: Neu. Statistical Analysis of Microbiome Data with R | Yinglin Xia (u. a.) | Taschenbuch | ICSA Book Series in Statistics | xxiii | Englisch | 2018 | Springer | EAN 9789811346453 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Hardback. Condición: New. 2018 ed. This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors' research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.…

Condición: New. In English.

Hardcover. Condición: new. Hardcover. Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field.Presents a thorough overview of machine learning algorithms for microbiome statistics.Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models.Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering.Investigates and applies various cross-validation techniques step-by-step.Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data.Offers all related R codes and the datasets from the authors first-hand microbiome research and publicly available data. This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …