Statistical and Machine Learning Approaches for Network Analysis by Matthias Dehmer
(Wiley Series in Computational Statistics)

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Synopsis

Explore the multidisciplinary nature of complex networks through machine learning techniques

Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks.

Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include:

A survey of computational approaches to reconstruct and partition biological networksAn introduction to complex networks—measures, statistical properties, and modelsModeling for evolving biological networksThe structure of an evolving random bipartite graphDensity-based enumeration in structured dataHyponym extraction employing a weighted graph kernel

Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

 

About Matthias Dehmer

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Matthias Dehmer studied mathematics at the University of Siegen, Germany and received his PhD in computer science from the Technical University of Darmstadt. He began his academic career as a research fellow at Vienna Bio Center, Austria, and at Vienna University of Technology, and is currently a professor for bioinformatics and systems biology at UMIT ? The Health and Life Sciences University. His research interests are in bioinformatics, systems biology, complex networks and statistics. In particular, Professor Dehmer is also working on machine learning-based methods to design new data analysis methods for solving problems in computational and systems biology. Frank Emmert-Streib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral research associate at the Stowers Institute for Medical Research, Kansas City, USA, and a Senior Fellow at the University of Washington, Seattle, USA. Currently, he is a lecturer/assistant professor at the Queen?s University Belfast, UK at the Center for Cancer Research and Cell Biology, heading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology and biostatistics in the development and application of methods from statistics and machine learning for the analysis of high-throughput data from genomics and genetics experiments. Armindo Salvador studied biochemistry at the University of Lisbon, Portugal, where he received his PhD in theoretical biochemistry. He was a postdoctoral fellow at the University of Michigan, USA, and at the University of Coimbra, Portugal. He currently heads the Molecular Systems Biology Group at the Center for Neuroscience and Cell Biology, University of Coimbra, Portugal. Dr. Salvador?s research interests are in the fields of molecular systems biology and computational biology. In particular, he is working toward clarifying the naturally evolved design principles of metabolic networks. Prior to joining Novartis Oncology in 2010, Armin Graber served as the CEO and Chancellor of the University for Health Sciences, Medical Informatics, and Technology (UMIT), in Hall, Austria, where he was also professor in the Department of Biomedical Sciences and Engineering. He has held various senior positions in biotechnology in the USA and Europe, including VP for Translational Research at BG Medicine, CEO of Biocrates life sciences AG, and Head of Bioinformatics in the Applied Biosystems Discovery Proteomics and Small Molecule Research Center. His research interests comprise targeted and non-targeted functional genomic technologies, and bioinformatic and biostatistic methods for biomarker discovery, validation and delivery.
 
Published June 26, 2012 by Wiley. 344 pages
Genres: Science & Math, Computers & Technology, Professional & Technical. Non-fiction

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