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Книга Statistical Network Genetics Rongling Wu

Statistical Network Genetics

Evolutionary Game Theory in Action

Автор: Rongling Wu
Език: Английски език
Корици: С меки корици
Издател: ACADEMIC PR INC
Наличност: 50% вероятност
Ще претърсим света
145.92 € 285.39 лв
Statistical Network Genetics: Evolutionary Game Theory in Action offers an interdisciplinary integra...

Информация за книгата

Автор
Rongling Wu
Език
Английски език
Корици
Книга - С меки корици
Издадена
2024
страници
225
EAN
9780323988018
Enbook ID
39252057
Издател
ACADEMIC PR INC
Размери
191 x 235

Пълно описание

Statistical Network Genetics: Evolutionary Game Theory in Action offers an interdisciplinary integration of statistical genetics and evolutionary game theory using the latest data, codes, and computational functions. While classic statistical genetics attempts to identify and map individual key genes, proteins, or metabolites associated with complex traits, this book examines how each entity interacts with each other through this complex, yet well-orchestrated, set of networks for mediating phenotypic variation. Written by leading experts on game theory and statistical genetics, this book introduces elements from multiple disciplines, like community ecology, network theory, and physics theory, tying them in to statistical model examples. The book provides a platform for previously disjointed ideas and concepts of evolutionary game theory and its role in statistical genetics. It covers genetic and genomic networking across ecological, environmental, as well as evolutionary factors. Statistical Network Genetics: Evolutionary Game Theory in Action is the ideal resource for evolutionary and computational biologists, especially those seeking a thorough and current understanding of the connection to statistical genetics. Examines and describes state-of-the-art approaches for maximizing statistical genetic research Provides usable computer codes for readers to practice statistical methods described in the chapters Features expert analysis of high-dimensional data for linking genotypes to phenotypes, emphasizing the role of omics data to understanding phenotypic formation

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