Catalogue


Wertverteilung meromorpher Funktionen in ein- und mehrfach zusammenhängenden Gebieten [electronic resource] /
Alexander Dinghas ; hrsg. von R. Nevanlinna und C. Andreian Cazacu.
imprint
Berlin ; New York : Springer, 1980.
description
xiii, 145 p. ; 25 cm.
ISBN
0387097597
format(s)
Book
More Details
imprint
Berlin ; New York : Springer, 1980.
isbn
0387097597
restrictions
Licensed for access by U. of T. users.
general note
Includes index.
catalogue key
8192471
 
Bibliography: p. [132]-140.
A Look Inside
Reviews
Review Quotes
From the reviews: "The editors of this handbook ... have 'collect(ed) together, in one place, the algorithms and methods that form the modern bioinformatician's toolkit.' ... Each chapter contains an introduction to the respective topic, detailed descriptions of the relevant algorithmic methods, software implementations, and applications, and concludes with advanced topics. ... this handbook provides some value for researchers and practitioners in the field. ... In summary, for most of the topics covered, this book complements the standard textbooks in a useful way." (Gunnar Klau, ACM Computing Reviews, June, 2011)
From the reviews:The editors of this handbook … have 'collect(ed) together, in one place, the algorithms and methods that form the modern bioinformatician's toolkit.' … Each chapter contains an introduction to the respective topic, detailed descriptions of the relevant algorithmic methods, software implementations, and applications, and concludes with advanced topics. … this handbook provides some value for researchers and practitioners in the field. … In summary, for most of the topics covered, this book complements the standard textbooks in a useful way. (Gunnar Klau, ACM Computing Reviews, June, 2011)
To find out how to look for other reviews, please see our guides to finding book reviews in the Sciences or Social Sciences and Humanities.
Summaries
Bowker Data Service Summary
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.
Main Description
Bioinformatics is constantly evolving, but a core body of algorithmic ideas for a problem-solving approach in the field has emerged. This handbook stresses that approach as it covers all relevant areas of computational biology and bioinformatics.
Main Description
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. 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.
Main Description
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.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.
Main Description
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.The 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.
Table of Contents
Sequences
Modern BLAST Programsp. 3
Introductionp. 3
Available Implementationsp. 4
Algorithm Descriptionp. 6
BLAST Statisticsp. 9
Examplesp. 10
Advanced Topicsp. 14
Exercisesp. 17
Further Readingp. 18
Referencesp. 18
Practical Multiple Sequence Alignmentp. 21
History of the Problemp. 21
Algorithm Descriptionp. 24
Available Implementationsp. 36
Advanced Topicsp. 37
Exercisesp. 38
Further Readingp. 39
Referencesp. 40
Sequence Alignment Statisticsp. 45
Introductionp. 46
Dynamic Programming with Independent Random Inputsp. 47
The Extreme-Value Distributions for Maximap. 49
The Poisson Approximation for Counting Rare Eventsp. 50
Pairwise Sequence Alignmentp. 51
Islands in Local Alignmentp. 53
The Finite-Size Correction in Gapped Local Alignmentp. 54
The Independent Diagonals Approximationp. 55
The Combinatorial Approximationp. 56
Referencesp. 58
Phylogenetics
Practical Implications of Coalescent Theoryp. 63
Introduction - What is the coalescent?p. 63
Motivating Problemsp. 66
Implications of the Coalescentp. 69
Software Reviewp. 76
Further Readingp. 79
Exercises for the readerp. 79
Referencesp. 80
Graph Model of Coalescence with Recombinationsp. 85
Introductionp. 85
Random Graph Framework: Pedigree Graphp. 87
Pedigree Subgraphsp. 90
Unilinear Transmission: Monochromatic Subgraphsp. 92
Genetic Exchange Model: Mixed Subgraphp. 93
Topological definition of GMRCA: Least Common Ancestor with Ancestry (LCAA)p. 96
Discussionp. 97
Referencesp. 99
Phylogenetic Trees From Sequencesp. 101
Basicsp. 101
Sequence evolutionp. 106
Distance-based phylogeny reconstructionp. 110
Maximum parsimonyp. 114
Maximum likelihoodp. 115
Multiple phylogenies: comparison, consensus, and confidencep. 117
Softwarep. 121
Further readingp. 121
Exercisep. 122
Referencesp. 123
Evolutionary Phylogenetic Networks: Models and Issuesp. 125
Introductionp. 125
Phylogenetic Networks and the Trees Withinp. 127
Optimization Criteria for Inferring and Evaluating Phylogenetic Networksp. 136
To Network, or Not to Network, That Is the Questionp. 145
Exercisesp. 153
Further Readingp. 153
Referencesp. 154
Genome Wide Association Studiesp. 159
An overview of population geneticsp. 159
Genome-Wide Association Studiesp. 162
Resourcesp. 172
Advanced Topicsp. 172
Exercisep. 173
Referencesp. 174
Proteins: Structure, Function, and Biochemistry
Novel Perspectives on Protein Structure Predictionp. 179
Introductionp. 179
Modeling transmembrane ß-barrel structurep. 182
Energy modelp. 186
Algorithmsp. 188
Applicationsp. 193
Sampling the local neighborhood of 3D structuresp. 200
Exercisesp. 203
Further readingp. 203
Referencesp. 204
Stochastic Simulation for Biochemical Systemsp. 209
History of the Problemp. 209
Algorithm Descriptionp. 211
Available Implementationsp. 216
Examplesp. 221
Advanced Topicsp. 223
Exercisesp. 227
Further Readingp. 228
Referencesp. 228
Networks
Cellular Response Networksp. 233
History of the Problemp. 233
Algorithm Descriptionsp. 234
Available Implementationsp. 244
Examplesp. 245
Advanced Topics: Comparing Response Networksp. 247
Exercisesp. 249
Outlook and Further Readingp. 250
Referencesp. 250
Identification of Modules in Protein-Protein Interaction Networksp. 253
History of the Problemp. 254
Algorithm Descriptionp. 255
Available Implementationsp. 261
Examplesp. 262
Advanced Topicsp. 263
Exercisesp. 264
Further Readingp. 265
Referencesp. 265
Biological Data Management and Mining
Designing Microarray Experimentsp. 271
Designed experiments versus observational studiesp. 271
Discovery of Differentially Expressed Genesp. 274
Building prognostic modelsp. 279
Running the experimentp. 281
Advanced topics and further readingp. 285
Exercisep. 286
Referencesp. 287
Matrix and Tensor Decompositionsp. 291
Introductionp. 291
Non-negative Matrix Factorizationp. 292
Singular Value Decompositionp. 295
Other Matrix Decompositionsp. 299
Comparison of the Methodsp. 302
Tensor Decompositionsp. 308
Concluding Remarksp. 310
Exercisesp. 311
Referencesp. 312
Practical Applications of the Gene Ontology Resourcep. 319
Introductionp. 319
GO Annotationsp. 320
Viewing the GO and its annotationsp. 324
Use of GO in the literaturep. 326
Popular methods for supplementing and grouping GO annotationsp. 328
The effective use of GO for large-scale analysesp. 334
Exercisesp. 335
Further Readingp. 338
Referencesp. 338
Indexp. 341
Table of Contents provided by Ingram. All Rights Reserved.

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