Analysis of Complex Networks: From Biology to Linguistics
Matthias Dehmer · Frank Emmert-Streib
Jul 2009 · John Wiley & Sons
Ebook
480
Pages
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About this ebook
Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.
About the author
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). Currently, he is with the Vienna University of Technology (Austria) in the department of Discrete Mathematics and Geometry. His research interests are Graph Theory, Information Theory, Computational Biology and Machine Learning.
Frank Emmert-Streib studied Physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics form the University of Bremen (Germany). He was postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and is currently senior research fellow at the University of Washington (Seattle, USA) in Biostatistics and Genome Sciences. His research interests are in the field of Computational Biology, Biostatistics, Machine Learning and Information Theory and Statistical Learning.
Frank Emmert-Streib studied Physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics form the University of Bremen (Germany). He was postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and is currently senior research fellow at the University of Washington (Seattle, USA) in Biostatistics and Genome Sciences. His research interests are in the field of Computational Biology, Biostatistics, Machine Learning and Information Theory and Statistical Learning.
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