- Series: Chapman & Hall/CRC Big Data Series
- Hardcover: 332 pages
- Publisher: Chapman and Hall/CRC; 1 edition (August 18, 2016)
- Language: English
- ISBN-10: 1498723616
- ISBN-13: 978-1498723619
- Product Dimensions: 7 x 0.9 x 10.1 inches
- Shipping Weight: 1.9 pounds (View shipping rates and policies)
- Average Customer Review: Be the first to review this item
- Amazon Best Sellers Rank: #5,280,603 in Books (See Top 100 in Books)
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Big Data of Complex Networks (Chapman & Hall/CRC Big Data Series) 1st Edition
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About the Author
Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his PhD in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna BioCenter (Austria), Vienna University of Technology, and University of Coimbra (Coimbra). 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 Universit¨at der Bundeswehr M¨unchen. His research interests are in applied mathematics, bioinformatics, systems biology, graph theory, complexity, and information theory. He has written over 175 publications in his research areas.
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, machine learning, 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.
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