Xem 1-20 trên 294 kết quả Biological data
  • This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data.

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  • Bioinformatics is an interdisciplinary field which addresses biological problems using computational techniques, and makes the rapid organization and analysis of biological data possible. The field may also be referred to as computational biology, and can be defined as, "conceptualizing biology in terms of molecules and then applying informatics techniques to understand and organize the information associated with these molecules, on a large scale.

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  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Exact distribution of a pattern in a set of random sequences generated by a Markov source: applications to biological data...

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  • Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo...

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  • As you worked your way through high school, or otherwise worked to prepare yourself for college, you were probably unaware that an information explosion was taking place in the field of biology. This explosion, brought on by advances in biotechnology and communicated by faster, more powerful computers, has allowed scientists to gather data more quickly and disseminate data to colleagues in the global scientific community with the click of a mouse.

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  • MATLAB (Matrix Laboratory) is a matrix-oriented tool for mathematical programming, applied for numerical computation and simulation purposes. Together with its dynamic simulation toolbox Simulink, as a graphical environment for the simulation of dynamic systems, it has become a very powerful tool suitable for a large number of applications in many areas of research and development.

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  • Managing and Mining Graph Data part 1 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • (USGS) is to assess the quantity and quality of the natural resources of the Nation and to provide information that will assist resource managers and policymakers at Federal, State, and local levels in making sound decisions. Assessment of water-quality conditions and trends is an important part of this overall mission. One of the greatest challenges faced by waterresources scientists is acquiring reliable information that will guide the use and protection of the Nation’s water resources.

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  • Managing and Mining Graph Data part 3 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • The biological sciences have become more quantitative and information-driven since emerging computational and mathematical tools facilitate collection and analysis of vast amounts of biological data. Complexity analysis of biological systems provides biological knowledge for the organization, management, and mining of biological data by using advanced computational tools. The biological data are inherently complex, nonuniform, and collected at multiple temporal and spatial scales.

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  • Bioinformatics is a growing multidisciplinary field of science comprising biology, computer science, and mathematics. It is the theoretical and computational arm of modern biology. In other words, bioinformatics is a tool in the hands of biologists for analyzing huge amount of biological data available on mainstream public databases. Currently, bioinformatics has gained variety of applications in agriculture, medicine, engineering, and natural science. This book discusses a small portion of these applications along with basic concepts and fundamental techniques in bioinformatics....

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  • Managing and Mining Graph Data part 62 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 2 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • During the past two decades, reductionist biological science has generated new empirical data on the molecular foundations of biological structure and function at an accelerating rate. The list of organisms whose complete genomes have been sequenced is growing by the week. Annotations of these sequences are becoming more comprehensive, and databases of protein structure are growing at impressive, indeed formerly unimaginable rates.

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  • Managing and Mining Graph Data part 5 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 6 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 11 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • Managing and Mining Graph Data part 61 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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  • The field of medical informatics has grown rapidly over the past decade due to the advances in biomedical computing, the abundance of biomedical and genomic data, the ubiquity of the Internet, and the general acceptance of computing in various aspects of medical, biological, and health care research and practice. This book aims to be complementary to several other popular introductory medical informatics textbooks.

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  • Managing and Mining Graph Data part 4 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area.

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