Book description
This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.
Table of contents
- Cover
- Half Title page
- Title page
- Copyright page
- Dedication
- Preface
- Contributors
- Series page
-
Part I: Strings Processing and Application to Biological Sequences
- Chapter 1: String Data Structures for Computational Molecular Biology
- Chapter 2: Efficient Restricted-Case Algorithms for Problems in Computational Biology
- Chapter 3: Finite Automata in Pattern Matching
- Chapter 4: New Developments in Processing of Degenerate Sequences
- Chapter 5: Exact Search Algorithms for Biological Sequences
- Chapter 6: Algorithmic Aspects of Arc-Annotated Sequences
-
Chapter 7: Algorithmic Issues in DNA Barcoding Problems
- 7.1 Introduction
- 7.2 Test Set Problems: A General Framework for Several Barcoding Problems
- 7.3 A Synopsis of Biological Applications of Barcoding
- 7.4 Survey of Algorithmic Techniques on Barcoding
- 7.5 Information Content Approach
- 7.6 Set-Covering Approach
- 7.7 Experimental Results and Software Availability
- 7.8 Concluding Remarks
- Acknowledgments
- References
- Chapter 8: Recent Advances in Weighted DNA Sequences
- Chapter 9: DNA Computing for Subgraph Isomorphism Problem and Related Problems
-
Part II: Analysis of Biological Sequences
- Chapter 10: Graphs in Bioinformatics
- Chapter 11: A Flexible Data Store for Managing Bioinformatics Data
- Chapter 12: Algorithms for the Alignment of Biological Sequences
-
Chapter 13: Algorithms for Local Structural Alignment and Structural Motif Identification
- 13.1 Introduction
- 13.2 Problem Definition of Local Structural Alignment
- 13.3 Variable-Length Alignment Fragment Pair (VLAFP) Algorithm
- 13.4 Structural Alignment Based on Center of Gravity: SACG
- 13.5 Searching Structural Motifs
- 13.6 Using SACG Algorithm for Classification of New Protein Structures
- 13.7 Experimental Results
- 13.8 Accuracy Results
- 13.9 Conclusion
- Acknowledgments
- References
- Chapter 14: Evolution of the Clustal Family of Multiple Sequence Alignment Programs
- Chapter 15: Filters and Seeds Approaches for Fast Homology Searches in Large Datasets
-
Chapter 16: Novel Combinatorial and Information-Theoretic Alignment-Free Distances Biological Data Mining
- 16.1 Introduction
- 16.2 Information-Theoretic Alignment-Free Methods
- 16.3 Combinatorial Alignment-Free Methods
- 16.4 Alignment-Free Compositional Methods
- 16.5 Alignment-Free Exact Word Matches Methods
- 16.6 Domains of Biological Application
- 16.7 Datasets and Software for Experimental Algorithmics
- 16.8 Conclusions
- References
- Chapter 17: In Silico Methods for the Analysis of Metabolites and Drug Molecules
-
Part III: Motif Finding and Structure Prediction
- Chapter 18: Motif Finding Algorithms in Biological Sequences
-
Chapter 19: Computational Characterization of Regulatory Regions
- 19.1 The Genome Regulatory Landscape
- 19.2 Qualitative Models of Regulatory Signals
- 19.3 Quantitative Models of Regulatory Signals
- 19.4 Detection of Dependencies in Sequences
- 19.5 Repositories of Regulatory Information
- 19.6 Using Predictive Models to Annotate Sequences
- 19.7 Comparative Genomics Characterization
- 19.8 Sequence Comparisons
- 19.9 Combining Motifs and Alignments
- 19.10 Experimental Validation
- 19.11 Summary
- References
- Chapter 20: Algorithmic Issues in the Analysis of Chip-SEQ Data
- Chapter 21: Approaches and Methods for Operon Prediction Based on Machine Learning Techniques
-
Chapter 22: Protein Function Prediction with Data-Mining Techniques
- 22.1 Introduction
- 22.2 Protein Annotation Based on Sequence
- 22.3 Protein Annotation Based on Protein Structure
- 22.4 Protein Function Prediction Based on Gene Expression Data
- 22.5 Protein Function Prediction Based on Protein Interactome Map
- 22.6 Protein Function Prediction Based on Data Integration
- 22.7 Conclusions and Perspectives
- References
- Chapter 23: Protein Domain Boundary Prediction
- Chapter 24: An Introduction to RNA Structure and Pseudoknot Prediction
- Part IV: Phylogeny Reconstruction
-
Part V: Microarray Data Analysis
- Chapter 28: Microarray Gene Expression Data Analysis
- Chapter 29: Biclustering of Microarray Data
- Chapter 30: Computational Models for Condition-Specific Gene and Pathway Inference
-
Chapter 31: Heterogeneity of Differential Expression in Cancer Studies: Algorithms and Methods
- 31.1 Introduction
- 31.2 Notations
- 31.3 Differential Mean of Expression
- 31.4 Differential Variability of Expression
- 31.5 Differential Expression in Compendium of Tumors
- 31.6 Differential Expression by Chromosomal Aberrations: The Local Properties
- 31.7 Differential Expression in Gene Interactome
- 31.8 Differential Coexpression: Global Multidimensional Interactome
- Acknowledgments
- References
- Part VI: Analysis of Genomes
-
Part VII: Analysis of Biological Networks
- Chapter 37: Untangling Biological Networks Using Bioinformatics
- Chapter 38: Probabilistic Approaches for Investigating Biological Networks
- Chapter 39: Modeling and Analysis of Biological Networks with Model Checking
- Chapter 40: Reverse Engineering of Molecular Networks from a Common Combinatorial Approach
-
Chapter 41: Unsupervised Learning for Gene Regulation Network Inference from Expression Data: A Review
- 41.1 Introduction
- 41.2 Gene Networks: Definition and Properties
- 41.3 Gene Expression: Data and Analysis
- 41.4 Network Inference as an Unsupervised Learning Problem
- 41.5 Correlation-Based Methods
- 41.6 Probabilistic Graphical Models
- 41.7 Constraint-Based Data Mining
- 41.8 Validation
- 41.9 Conclusion and Perspectives
- References
- Chapter 42: Approaches to Construction and Analysis of Microrna-Mediated Networks
- Index
Product information
- Title: Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications
- Author(s):
- Release date: February 2011
- Publisher(s): Wiley
- ISBN: 9780470505199
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