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Discovering Biomolecular Mechanisms with Computational Biology [recurso electrónico] / by Frank Eisenhaber.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Molecular Biology Intelligence UnitEditor: Boston, MA : Springer US, 2006Descripción: XI, 147p. 42 illus., 1 illus. in color. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • recurso en línea
ISBN:
  • 9780387367477
  • 99780387367477
Tema(s): Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD:
  • 610 23
Recursos en línea:
Contenidos:
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.
En: Springer eBooksResumen: 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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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.

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