Details

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.

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