(Ebook) Mathematical Foundations and Applications of Graph Entropy by Matthias Dehmer, Frank Emmert-Streib, Zengqiang Chen, Xueliang Li, Yongtang Shi (Editors) ISBN 9783527339099, 9783527693221, 9783527693245, 3527339094, 352769322X, 3527693246, B01J6V1WXC

(Ebook) Mathematical Foundations and Applications of Graph Entropy by Matthias Dehmer, Frank Emmert-Streib, Zengqiang Chen, Xueliang Li, Yongtang Shi (Editors) ISBN 9783527339099, 9783527693221, 9783527693245, 3527339094, 352769322X, 3527693246, B01J6V1WXC

Main subject categories: • Mathematics - Graph theory • Mathematics - Graph entropy • Mathematical statistics • Probability • Biology • Mathematical biologyThis latest addition to the successful Network Biology series presents current methods for determining the entropy of networks, making it the first to cover the recently established Quantitative Graph Theory.An excellent international team of editors and contributors provides an up-to-date outlook for the field, covering a broad range of graph entropy-related concepts and methods. The topics range from analyzing mathematical properties of methods right up to applying them in real-life areas.Filling a gap in the contemporary literature this is an invaluable reference for a number of disciplines, including mathematicians, computer scientists, computational biologists, and structural chemists.The book introduces to the reader a number of cutting-edge statistical methods which can be used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.