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Matthias (Hrsg.) Dehmer

Statistical Diagnostics for Cancer

Analyzing High-Dimensional Data

Ebook (EPUB Format)

This ready reference discusses different methods for statistically analyzing and validating cancer data generated from high-throughput methods. In contrast to other titles, this book focuses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more orless complex biological network. Due to the complex nature of cancer, such approaches are very appropriate.From a methodological point of view, the well-balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore,… Mehr

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Produktdetails


Weitere Autoren: Emmert-Streib, Frank (Reihe Hrsg.)
  • ISBN: 978-3-527-66545-7
  • EAN: 9783527665457
  • Produktnummer: 16373326
  • Verlag: Wiley-Blackwell
  • Sprache: Englisch
  • Erscheinungsjahr: 2012
  • Seitenangabe: 312 S.
  • Plattform: EPUB
  • Masse: 5'802 KB

Über den Autor


Frank Emmert-Streib studied physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics from the University of Bremen (Germany). He was a postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and a Senior Fellow at the University of Washington (Seattle, USA) in the Department of Biostatistics and the Department of Genome Sciences. Currently, he is Lecturer/Assistant Professor at the Queen's University Belfast at the Center for Cancer Research and Cell Biology (CCRCB) leading 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. 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 Bio Center (Austria), Vienna University of Technology and University of Coimbra (Portugal). Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria). His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry.

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