Matthias Dehmer Knihy

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.

This latest addition to the successful Network Biology series presents current methods for determining the entropy of networks, making it the first to cover the recently established Quantitative Graph Theory. An excellent international team of editors and contributors provides an up-to-date outlook for the field, covering a broad range of graph entropy-related concepts and methods. The topics range from analyzing mathematical properties of methods right up to applying them in real-life areas. Filling a gap in the contemporary literature this is an invaluable reference for a number of disciplines, including mathematicians, computer scientists, computational biologists, and structural chemists.

This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

A well-balanced overview of mathematical approaches to complex systems ranging from applications in chemistry and ecology to basic research questions on network complexity. Matthias Dehmer, Abbe Mowshowitz, and Frank Emmert-Streib, well-known pioneers in the fi eld, have edited this volume with a view to balancing classical and modern approaches to ensure broad coverage of contemporary research problems. The book is a valuable addition to the literature and a must-have for anyone dealing with network compleaity and complexity issues.

This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.

The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.

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.

Das Web Mining, welches aus den Teilgebieten Web Structure Mining, Web Content Mining und Web Usage Mining besteht, erlangt im Zuge der Web-basierten Kommunikation eine immer stärkere Bedeutung. Aufgrund unüberschaubarer Datenmengen im Web sind gerade leistungsfähige Verfahren zur Gewinnung und Analyse Web-basierter Informationen von großer Wichtigkeit. Matthias Dehmer rückt das Web Structure Mining, insbesondere die strukturelle Analyse Web-basierter Hypertexte auf Grundlage gerichteter Graphen, in den Mittelpunkt seiner Untersuchung. Der Autor stellt ein graphentheoretisches Modell zur Bestimmung der strukturellen Ähnlichkeit einer Klasse von gerichteten Graphen vor. Auf Basis des angesprochenen Modells führt er Experimente mit bestehenden Hypertexten durch und beschreibt neuartige Anwendungen im Web Structure Mining und in anderen Gebieten.

Die Funktionentheorie ist eine faszinierende Teildisziplin der Mathematik, die durch ihre Schönheit und Vielfältigkeit besticht. Ein zentrales Thema sind komplexwertige Polynome, die als holomorphe Funktionen betrachtet werden. Die analytische Theorie dieser Polynome untersucht deren Eigenschaften und wird oft als Geometrie der Polynome bezeichnet, da sie geometrische Beziehungen zwischen Nullstellen und Koeffizienten analysiert. Über Jahrzehnte hinweg haben Mathematiker bedeutende Probleme in diesem Bereich erforscht. Das Buch behandelt ein zentrales Problem der analytischen Theorie: die Bestimmung von Schranken für die Beträge der Nullstellen komplexwertiger Polynome. Es bietet eine umfassende und verständliche Darstellung vieler bekannter und historischer Ergebnisse, einschließlich grundlegender Untersuchungen sowie spezifischer Themen wie das Landau-Montel Problem. Es richtet sich an Mathematiker, Physiker und Informatiker, die in diesem Bereich forschen oder praktische Probleme lösen, sowie an interessierte Studierende der genannten Fachdisziplinen.