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Książka Statistical Network Genetics Rongling Wu

Statistical Network Genetics

Evolutionary Game Theory in Action

Autor: Rongling Wu
Język: Angielski
Oprawa: Miękka
Wydawca: ACADEMIC PR INC
Dostępność: 50 % szansa
Przeszukamy cały świat
617.72 zł
Statistical Network Genetics: Evolutionary Game Theory in Action offers an interdisciplinary integra...

Informacje o książce

Autor
Rongling Wu
Język
Angielski
Oprawa
Książka - Miękka
Data wydania
2024
strony
225
EAN
9780323988018
Enbook ID
39252057
Wydawca
ACADEMIC PR INC
Wymiary
191 x 235

Pełny opis

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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