Discovering Biomolecular Mechanisms with Computational Biology /
by Frank Eisenhaber.
- xI, 147 páginas, 42 ilustraciones, 1 ilustraciones en color. recurso en línea.
- Molecular Biology Intelligence Unit .
Springer eBooks
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.
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.