Eisenhaber | Discovering Biomolecular Mechanisms with  Computational Biology | Buch | 978-1-4419-4177-0 | www.sack.de

Buch, Englisch, 147 Seiten, Previously published in hardcover, Format (B × H): 165 mm x 248 mm, Gewicht: 285 g

Reihe: Molecular Biology Intelligence Unit

Eisenhaber

Discovering Biomolecular Mechanisms with Computational Biology


1. Auflage. Softcover version of original hardcover Auflage 2006
ISBN: 978-1-4419-4177-0
Verlag: Springer US

Buch, Englisch, 147 Seiten, Previously published in hardcover, Format (B × H): 165 mm x 248 mm, Gewicht: 285 g

Reihe: Molecular Biology Intelligence Unit

ISBN: 978-1-4419-4177-0
Verlag: Springer US


This anthology presents critical reviews of methods and high-impact applications in computational biology that lead to results that non-bioinformaticians must also know to design efficient experimental research plans. Discovering Biomolecular Mechanisms with Computational Biology explores the methodology of translating sequence strings into biological knowledge and considers exemplary groundbreaking results such as unexpected enzyme discoveries. This book also summarizes non-trivial theoretical predictions for regulatory and metabolic networks that have received experimental confirmation.

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Zielgruppe


Research


Autoren/Hrsg.


Weitere Infos & Material


Prediction of Post-translational modifications from amino acid sequence: Problems, pitfalls, methodological hints.- Deriving Biological Function of Genome Information with Biomolecular Sequence and Structure Analysis.- Reliable and Specific Protein Function Prediction by Combining Homology with Genomic(s) Context.- Clues from Three-Dimensional Structure Analysis and Molecular Modelling.- Prediction of Protein Function.- Complementing Biomolecular Sequence Analysis with Text Mining in Scientific Articles.- Extracting Information for Meaningful Function Inference through Text-Mining.- Literature and Genome Data Mining for Prioritizing Disease-Associated Genes.- Mechanistic Predictions from the Analysis of Biomolecular Networks.- Model-Based Inference of Transcriptional Regulatory Mechanisms from DNA Microarray Data.- The Predictive Power of Molecular Network Modelling.- Mechanistic Predictions from the Analysis of Biomolecular Sequence Populations: Considering Evolution for Function Prediction.- Theory of Early Molecular Evolution.- Hitchhiking Mapping.- Understanding the Functional Importance of Human Single Nucleotide Polymorphisms.- Correlations between Quantitative Measures of Genome Evolution, Expression and Function.



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