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This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.…mehr

Produktbeschreibung
This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics.
With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping.
Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.
  • Produktdetails
  • Quantitative and Network Biology
  • Verlag: Wiley-VCH
  • Artikelnr. des Verlages: 1133958 000
  • 1. Auflage
  • Erscheinungstermin: 15. September 2016
  • Englisch
  • Abmessung: 251mm x 175mm x 25mm
  • Gewicht: 938g
  • ISBN-13: 9783527339587
  • ISBN-10: 3527339582
  • Artikelnr.: 47218512
Autorenporträt
Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology, and University of Coimbra (Portugal). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) and also holds a position at the Universität der Bundeswehr München. His research interests are in applied mathematics, bioinformatics, systems biology, graph theory, complexity and information theory. He has written over 180 publications in his research areas. Yongtang Shi studied mathematics at Northwest University (Xi'an, China) and received his Ph.D in applied mathematics from Nankai University (Tianjin, China). He visited Technische Universität Bergakademie Freiberg (Germany), UMIT (Austria) and Simon Fraser University (Canada). Currently, he is an associate professor at the Center for Combinatorics of Nankai University. His research interests are in graph theory and its applications, especially the applications of graph theory in mathematical chemistry, computer science and information theory. He has written over 40 publications in graph theory and its applications. Frank Emmert-Streib studied physics at the University of Siegen (Germany) gaining his PhD in theoretical physics from the University of Bremen (Germany). He received postdoctoral training from the Stowers Institute for Medical Research (Kansas City, USA) and the University of Washington (Seattle, USA). Currently, he is associate professor for Computational Biology at Tampere University of Technology (Finland). His main research interests are in the field of computational medicine, network biology and statistical genomics.
Inhaltsangabe
Differential correlation technique to analyze biological networks: DiffCorr
Challenges of computational network analysis with R
Software and practices for visualizing network data in biology and medicine
Efficient anomaly detection in dynamic, attributed graphs by using R
Chemical informatics functionality in R
Biological network comparison
Degradation analysis in R using uDEMO
Penalized methods in high-dimensional Gaussian graphical models