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 qproblem solvingq approach to bioinformatics, wherein they use solutions to well-abstracted problems as building blocks to solve larger scope problems. Problem Solving Handbook for Computational Biology and Bioinformatics is an edited volume contributed by world renowned leaders in this field. This comprehensive handbook with problem solving emphasis, covers all relevant areas of computational biology and bioinformatics. Web resources and related themes are highlighted at every opportunity in this central easy-to-read reference. Designed for advanced-level students, researchers and professors in computer science and bioengineering as a reference or secondary text, this handbook is also suitable for professionals working in this industry.Please see Figure 7 for examples. (a) (b) (c) Fig. 8 A rogue taxon example: (a). Fig. 7 Consensus trees.(a1)-(a3) the three input trees; (b) table of nontrivial bipartitions; (c) the strict consensus; (d) the majority consensus; (e) the maximum anbsp;...
|Title||:||Problem Solving Handbook in Computational Biology and Bioinformatics|
|Author||:||Lenwood S. Heath, Naren Ramakrishnan|
|Publisher||:||Springer Science & Business Media - 2010-10-20|