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Discovering Biomolecular Mechanisms with Computational Biology de Frank Eisenhaber

Discovering Biomolecular Mechanisms with Computational Biology: Molecular Biology Intelligence Unit

Editat de Frank Eisenhaber
en Limba Engleză Paperback – 19 noi 2010
In this anthology, leading researchers present critical reviews of methods and high-impact applications in computational biology that lead to results that also non-bioinformaticians must know to design efficient experimental research plans. Discovering Biomolecular Mechanisms with Computational Biology also summarizes non-trivial theoretical predictions for regulatory and metabolic networks that have received experimental confirmation.
Discovering Biomolecular Mechanisms with Computational Biology is essential reading for life science researchers and higher-level students that work on biomolecular mechanisms and wish to understand the impact of computational biology for their success.
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Specificații

ISBN-13: 9781441941770
ISBN-10: 1441941770
Pagini: 160
Ilustrații: XI, 147 p. 42 illus., 1 illus. in color.
Dimensiuni: 165 x 248 x 8 mm
Ediția:Softcover reprint of hardcover 1st ed. 2006
Editura: Springer Us
Colecția Springer
Seria Molecular Biology Intelligence Unit

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

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.

Caracteristici

Contributors examine how sequence analysis becomes even more powerful if it is combined with automated scientific text mining (for the prediction of gene function and gene-disease association), with the analysis of expression data or allele occurrences (single-nucleotide polymorphisms) and frequencies Summarizes non-trivial theoretical predictions for regulatory and metabolic networks that have received experimental confirmation Includes supplementary material: sn.pub/extras