Description
Machine Learning for Microbiome Statistics provides researchers and practitioners with essential statistical methods and machine learning techniques tailored for microbiome analysis. Written by experts Yinglin Xia and Jun Sun, this Chapman & Hall/CRC Biostatistics volume covers both foundational concepts and advanced applications in microbiome research.
The book addresses unique challenges in microbiome data, including high dimensionality, compositionality, and sparsity. It presents practical approaches using modern machine learning algorithms, statistical modeling, and computational tools. Topics include data preprocessing, feature selection, classification, clustering, and visualization techniques specifically optimized for microbial community data.
Ideal for biostatisticians, bioinformaticians, and microbiologists, this resource combines theoretical understanding with hands-on implementation guidance. The text integrates real-world examples and case studies to illustrate how to apply these methods to actual microbiome datasets, making it invaluable for both students and experienced researchers.
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