Problem Solving Handbook in Computational Biology and Bioinformatics

Bioinformatics is growing by leaps and bounds; theories/algorithms/statistical techniques are constantly evolving. Nevertheless, a core body of algorithmic ideas have emerged and researchers are beginning to adopt a "problem solving" approach to bioinformatics, wherein they use solutions to well-abstracted problems as building blocks to solve larger scope problems.

Brings together, in one place,

Preface.- Pairwise Sequence Alignment.- Sequence and Sequence Alignment Statistics.- Practical Multiple Sequence Alignment.- Phylogenetic Trees from Sequences.- Phylogenetic Networks.- Modern BLAST Programs.- Stochastic Calculus and Stochastic Simulation.- Genomic Sequence Comparison.- Genome Rearrangements.- Population Genetics Data Analysis.- Genome Wide Association Studies.- Practical Implications of Coalescent Theory.- Networks in Computational Systems Biology.- Practical Use of the Gene Ontology.- Techniques for Protein Structure.- Microarray Experiment Statistics.- Reasoning with Protein Contact Maps .- Identifying Modules in Protein-Protein Interaction Networks.- Motifs for Transcriptional Regulation .- Time Course Analysis.- Computational Simulation of Biochemical Networks.- Biomedical Data Integration.- Functional Annotation and Discovery.- Matrix and Tensor Decompositions.- Text Processing and Mining.- Formal Logics.- Index.