Detailansicht
Network-based Molecular Biology
Data-driven Modeling and Analysis, De Gruyter Series in Mathematics and Life Sciences 3
ISBN/EAN: 9783110262568
Umbreit-Nr.: 5964998
Sprache:
Englisch
Umfang: X, 500 S., 20 s/w Illustr., 20 s/w Fotos, 50 s/w Z
Format in cm:
Einband:
gebundenes Buch
Erscheint am 15.01.2050
Auflage: 1/2050
- Zusatztext
- This book provides a comprehensive coverage of problems and solutions for integrating high-throughput experimental data with structured biological knowledge. It includes details of classical and novel graph-theoretic and statistical/probabilistic approaches allowing not only an adequate study of complex molecular networks, but also the possibility to posit and test biologically meaningful hypotheses. The text offers a comprehensive coverage of topics related to integration of data with structured knowledge in depth sufficient for carrying out independent network-based research in computational/systems biology and bioinformatics. The work is a suitable textbook for graduate students in bioinformatics, molecular biology, computational and systems biology, as well as a reference for researchers using network-based solutions for biological problems. The emphasis on network comparison and alignment and integration of high-throughput data with structured biological knowledge renders the book a timely addition to this rapidly growing research fields. This book can also be used as a supplementary text in applied mathematics with emphasis on graph theory, statistics, computer science, and machine learning.
- Kurztext
- The De Gruyter Series in Mathematics and Life Sciences is devoted to the publication of monographs in the field. They cover topics and methods in fields of current interest that use mathematical approaches to understand and explain, model and influence phenomena in all areas of life sciences. This includes, among others, theory and application of biological mathematical modeling, complex systems biology, bioinformatics, computational biomodeling stochastic modeling, biostatistics, computational evolutionary biology, comparative genomics, or structural bioinformatics. Also, new types of mathematical problems that arise from biological knowledge shall be covered.
- Autorenportrait
- Z. Nikoloski, MPI for Molecular Plant Physiology, Potsdam-Golm; S. Grimbs, Jacobs University Bremen.