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Preface.
Contributors.
Acknowledgments.
PART I: ALGORITHMS AND MODELS.
1 Parallel and Evolutionary Approaches to Computational Biology (Nouhad J. Rizk).
1.1 Introduction.
1.2 Bioinformatics.
1.3 Evolutionary Computation Applied to Computational Biology.
1.4 Conclusions.
References.
2 Parallel Monte Carlo Simulation of HIV Molecular Evolution in Response to Immune Surveillance (Jack da Silva).
2.1 Introduction.
2.2 The Problem.
2.3 The Model.
2.4 Parallelization with MPI.
2.5 Parallel Random Number Generation.
2.6 Preliminary Simulation Results.
2.7 Future Directions.
References.
3 Differential Evolutionary Algorithms for In Vivo Dynamic Analysis of Glycolysis and Pentose Phosphate Pathway in Escherichia coli (Christophe Chassagnole).
3.1 Introduction.
3.2 Mathematical Model.
3.3 Estimation of the Parameters of the Model.
3.4 Kinetic Parameter Estimation by DE.
3.5 Simulation and Results.
3.6 Stability Analysis.
3.7 Control Characteristic.
3.8 Conclusions.
References.
4 Compute-Intensive Simulations for Cellular Models (K. Burrage).
4.1 Introduction.
4.2 Simulation Methods for Stochastic Chemical Kinetics.
4.3 Aspects of Biology— Genetic Regulation.
4.4 Parallel Computing for Biological Systems.
4.5 Parallel Simulations.
4.6 Spatial Modeling of Cellular Systems.
4.7 Modeling Colonies of Cells.
References.
5 Parallel Computation in Simulating Diffusion and Deformation in Human Brain (Ning KangI0.
5.1 Introduction.
5.2 Anisotropic Diffusion Simulation in White Matter Tractography.
5.3 Brain Deformation Simulation in Image-Guided Neurosurgery.
5.4 Summary.
References.
PART II: SEQUENCE ANALYSIS AND MICROARRAYS.
6 Computational Molecular Biology (Azzedine Boukerche).
6.1 Introduction.
6.2 Basic Concepts in Molecular Biology.
6.3 Global and Local Biological Sequence Alignment.
6.4 Heuristic Approaches for Biological Sequence Comparison.
6.5 Parallel and Distributed Sequence Comparison.
6.6 Conclusions.
References.
7 Special-Purpose Computing for Biological Sequence Analysis (Bertil Schmidt).
7.1 Introduction.
7.2 Hybrid Parallel Computer.
7.3 Dynamic Programming Communication Pattern.
7.4 Performance Evaluation.
7.5 FutureWork and Open Problems.
7.6 Tutorial.
References.
8 Multiple Sequence Alignment in Parallel on a Cluster ofWorkstations (Amitava Datta).
8.1 Introduction.
8.2 CLUSTALW.
8.3 Implementation.
8.4 Results.
8.5 Conclusion.
References.
9 Searching Sequence Databases Using High-Performance BLASTs (Xue Wu).
9.1 Introduction.
9.2 Basic Blast Algorithm.
9.3 Blast Usage and Performance Factors.
9.4 High Performance BLASTs.
9.5 Comparing BLAST Performance.
9.6 UMD-BLAST.
9.7 Future Directions.
9.8 RelatedWork.
9.9 Summary.
References.
10 Parallel Implementations of Local Sequence Alignment: Hardware and Software (Vipin Chaudhary).
10.1 Introduction.
10.2 Sequence Alignment Primer.
10.3 Smith–Waterman Algorithm.
10.4 FASTA.
10.5 BLAST.
10.6 HMMER — Hidden Markov Models.
10.7 ClustalW.
10.8 Specialized Hardware: FPGA.
10.9 Conclusion.
References.
11 Parallel Computing in the Analysis of Gene Expression Relationships (Robert L. Martino).
11.1 Significance of Gene Expression Analysis.
11.2 Multivariate Gene Expression Relations.
11.3 Classification Based on Gene Expression.
11.4 Discussion and Future Directions.
References.
12 Assembling DNA Fragments with a Distributed Genetic Algorithm (Gabriel Luque).
12.1 Introduction.
12.2 DNA Fragment Assembly Problem.
12.3 DNA Fragment Assembly Using the Sequential GA.
12.4 DNA Fragment Assembly Problem Using the Parallel GA.
12.5 Experimental Results.
12.6 Conclusions.
References.
13 A Cooperative Genetic Algorithm for Knowledge Discovery in Microarray Experiments (Mohammed Khabzaoui).
13.1 Introduction.
13.2 Microarray Experiments.
13.3 Association Rules.
13.4 Multi-Objective Genetic Algorithm.
13.5 Cooperative Multi-Objective Genetic Algorithm (PMGA).
13.6 Experiments.
13.7 Conclusion.
References.
PART III: PHYLOGENETICS.
14 Parallel and Distributed Computation of Large Phylogenetic Trees (Alexandros Stamatakis).
14.1 Introduction.
14.2 Maximum Likelihood.
14.3 State-of-the-Art ML Programs.
14.4 Algorithmic Solutions in RAxML-III.
14.5 HPC Solutions in RAxML-III.
14.6 Future Developments.
References.
15 Phylogenetic Parameter Estimation on COWs (Ekkehard Petzold).
15.1 Introduction.
15.2 Phylogenetic Tree Reconstruction using Quartet Puzzling.
15.3 Hardware, Data, and Scheduling Algorithms.
15.4 Parallelizing PEst.
15.5 Extending Parallel Coverage in PEst.
15.6 Discussion.
References.
16 High-Performance Phylogeny Reconstruction Under Maximum Parsimony (Tiffani L. Williams).
16.1 Introduction.
16.2 Maximum Parsimony.
16.3 Exact MP: Parallel Branch and Bound.
16.4 MP Heuristics: Disk-Covering Methods.
16.5 Summary and Open Problems.
References.
PART IV: PROTEIN FOLDING.
17 Protein Folding with the Parallel Replica Exchange Molecular Dynamics Method (Ruhong Zhou).
17.1 Introduction.
17.2 REMD Method.
17.3 Protein Folding with REMD.
17.4 Protein Structure Refinement with REMD.
17.5 Summary.
References.
18 High-Performance Alignment Methods for Protein Threading (R. Andonov).
18.1 Introduction.
18.2 Formal Definition.
18.3 Mixed Integer Programming Models.
18.4 Divide-and-Conquer Technique.
18.5 Parallelization.
18.6 Future Research Directions.
18.7 Conclusion.
18.8 Summary.
References.
19 Parallel Evolutionary Computations in Discerning Protein Structures (Richard O. Day).
19.1 Introduction.
19.2 PSP Problem.
19.3 Protein Structure Discerning Methods.
19.4 PSP Energy Minimization EAs.
19.5 PSP Parallel EA Performance Evaluation.
19.6 Results and Discussion.
19.7 Conclusions and Suggested Research.
References.
PART V: PLATFORMS AND ENABLING TECHNOLOGIES.
20 A Brief Overview of Grid Activities for Bioinformatics and Health Applications (Ali Al Mazari).
20.1 Introduction.
20.2 Grid Computing.
20.3 Bioinformatics and Health Applications.
20.4 Grid Computing for Bioinformatics and Health Applications.
20.5 Grid Activities in Europe.
20.6 Grid Activities in the United Kingdom.
20.7 Grid Activities in the USA.
20.8 Grid Activities in Asia and Japan.
20.9 International Grid Collaborations.
20.10 International Grid Collaborations.
20.11 Conclusions and Future Trends.
References.
21 Parallel Algorithms for Bioinformatics (Shahid H. Bokhari).
21.1 Introduction.
21.2 Parallel Computer Architecture.
21.3 Bioinformatics Algorithms on the Cray MTA System.
21.4 Summary.
References.
22 Cluster and Grid Infrastructure for Computational Chemistry and Biochemistry (Kim K. Baldridge).
22.1 Introduction.
22.2 GAMESS Execution on Clusters.
22.3 Portal Technology.
22.4 Running GAMESS with Nimrod Grid-Enabling Infrastructure.
22.5 Computational ChemistryWorkflow Environments.
22.6 Conclusions.
References.
23 DistributedWorkflows in Bioinformatics (Arun Krishnan).
23.1 Introduction.
23.2 Challenges of Grid Computing.
23.3 Grid Applications.
23.4 Grid Programming.
23.5 Grid Execution Language.
23.6 GUI-BasedWorkflow Construction and Execution.
23.7 Case Studies.
23.8 Summary.
References.
24 Molecular Structure Determination on a Computational and Data Grid (Russ Miller).
24.1 Introduction.
24.2 Molecular Structure Determination.
24.3 Grid Computing in Buffalo.
24.4 Center for Computational Research.
24.5 ACDC-Grid Overview.
24.6 Grid Research Collaborations.
24.7 Grid Research Advancements.
24.8 Grid Research Application Abstractions and Tools.
24.9 Conclusions.
References.
25 GIPSY: A Problem-Solving Environment for Bioinformatics Applications (Rajendra R. Joshi).
25.1 Introduction.
25.2 Architecture.
25.3 Currently Deployed Applications.
25.4 Conclusion.
References.
26 TaskSpaces: A Software Framework for Parallel Bioinformatics on Computational Grids (Hans De Sterck).
26.1 Introduction.
26.2 The TaskSpaces Framework.
26.3 Application: Finding Correctly Folded RNA Motifs.
26.4 Case Study: Operating the Framework on a Computational Grid.
26.5 Results for the RNA Motif Problem.
26.6 FutureWork.
26.7 Summary and Conclusion.
References.
27 The Organic Grid: Self-Organizing Computational Biology on Desktop Grids (Arjav J. Chakravarti).
27.1 Introduction.
27.2 Background and RelatedWork.
27.3 Measurements.
27.4 Conclusions.
27.5 Future Directions.
References.
28 FPGA Computing in Modern Bioinformatics (H. Simmler).
28.1 Parallel Processing Models.
28.2 Image Processing Task.
28.3 FPGA Hardware Accelerators.
28.4 Image Processing Example.
28.5 Case Study: Protein Structure Prediction.
28.6 Conclusion.
References.
29 Virtual Microscopy: Distributed Image Storage, Retrieval, Analysis, and Visualization (T. Pan).
29.1 Introduction.
29.2 Architecture.
29.3 Image Analysis.
29.4 Clinical Use.
29.5 Education.
29.6 Future Directions.
29.7 Summary.
References.
Index.
| Price | Condition | Delivery | Seller | Action |
| $99.99 | Digital |
| WonderClub |
Rick Lam
reviewed Parallel computing for bioinformatics and computational biology on January 23, 2014
Parallel computing for bioinformatics and computational biology
I love this book. I've loved since I first read it 15 years ago. I love it for the way it tickled my imagination with the wonderful idea of entire civilisations living in the carpet. I love it for the standard fantasy adventure story that it is at its core but told in such a way and such a world that even I didn't yawn at the constant walking from one place to another of it all. I love that after all this time I still get excited by reading it and I love this shiny new illustrated edition complete with the authors own art from his teenage years. I love the introduction from the master storyteller slightly embarrassed by his early efforts yet not too big to realise the mistakes he made and fix some of them. And it is this that is most amazing to me at 29 as yet unpublished, that even the greats such as Terry Pratchett weren't masters instantly but kept on working at their craft even after being published.
Reading again after the journey through the Tiffany Aching stories and even his finest novel Nation, which just so happened to be primarily a young adult novel, it is clear that he always had an extraordinary ability to write adult stories that were accessible to children, that taught them without treating them as if they were stupid or making it an obvious lesson (something that I've only ever noticed in a Lemony Snicket book outside of Mr Pratchetts work, but sure I don't make a habit of reading kids books, even as a kid) and his ability to create easily identifiable, conventional characters and play with the readers expectations and the genre character rules.
Snibril is the protagonist who undertakes a journey with his companions, losing everything but gaining something so much more valuable; realising along the way such universal truths as the importance of education and friendship and the value of property. And I'm happy to keep taking the journey with him, time and time again.
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Add Parallel computing for bioinformatics and computational biology, Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful para, Parallel computing for bioinformatics and computational biology to your collection on WonderClub |