الصفحة 1
الصفحة 1
Network Analysis : Methodological Foundations
‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
Genetic programming IV : Routine human-competitive machine intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law
Clinical metabolomics applications in genetic diseases
Helps readers discover the forefront of personalized medicine on clinical metabolomics and its applications in genetic diseases. This comprehensive guide offers a functional relationship map between cell components and genetic variations in various diseases, providing insights that can be applied to personalized medicine. Covers the latest developments in metabolomics for health, with practical guidance for clinical experts looking to advance their laboratory techniques and career. The metabolomics profile is a powerful tool that has revolutionized our understanding of the relationship between genetics, clinical readouts, and disease outcomes. By integrating metabolomics with genomics and clinical phenotypes, the authors have developed diagnostic and prediction models that have vastly improved patient outcomes and deepened the understanding of disease mechanisms.
Bioinformatics of genome regulation and structure II
The conference was organized by the Laboratory of Theoretical Genetics, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia. The material covers the most recent topics in bioinformatics, including (i) regulatory genomic sequences: databases, knowledge bases, computer analysis, modeling, and recognition; (ii) large-scale genome analysis and functional annotation; (iii) gene structure detection and prediction; (iv) comparative and evolutionary genomics; (v) computer analysis of genome polymorphism and evolution; computer analysis and modeling of transcription, splicing, and translation; structural computational biology: structure- function organization of genomic DNA, RNA, and proteins; (vi) gene networks, signal transduction pathways, and genetically controlled metabolic pathways: databases, knowledge bases, computer analysis, and modeling; principles of organization, operation, and evolution; (vii) data warehousing, knowledge discovery and data mining; and (viii) analysis of basic patterns of genome operation, organization, and evolution.
