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Biological network analysis : trends, approaches, graphical theory and algorithms /
"Complex biological systems and their inter-relationships are represented as graphs or networks. The use of graphs to model biological aspects in computational biology, in bioinformatics and biomedicine is currently growing. Graphs enable researchers to easily model relations among objects. Cur...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, MA :
Elsevier,
[2020]
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| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | "Complex biological systems and their inter-relationships are represented as graphs or networks. The use of graphs to model biological aspects in computational biology, in bioinformatics and biomedicine is currently growing. Graphs enable researchers to easily model relations among objects. Currently in bioinformatics and systems biology there is a growing interest in the analysis of associations among biological molecules at a network level. A main topic of research in this area is represented by the inference and analysis of biological networks from experimental data. Often researchers aim to analyze differences of evolutions among different networks, i.e., networks representing different states of the same reality or networks coming from different species. Consequently, a wide variety of algorithms have been developed to analyze and compare networks. In particular, the comparison of networks is often performed through network alignment algorithms that rely on graph and subgraph isomorphisms. Biological Network Analysis considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The authors discuss various graph-theoretic and data analytics approaches used to analyze these networks with respect to tools, technologies, standards, algorithms, and databases available for generating, representing, and analyzing graphical data."--Back cover |
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| Descripción Física: | 1 online resource (189 pages) : illustrations |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9780128193518 0128193514 |