Settling the Intractability of Multiple Alignment 1st edition by Isaac Elias ISBN 3540206958 9783540206958

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Product details:

ISBN 10:       3540206958
ISBN 13: 978-3540206958
Author:         Isaac Elias
 

Settling the Intractability of Multiple Alignment 1st Table of contents:

• Introduction

  • 1.1 Overview of Multiple Sequence Alignment
  • 1.2 The Significance of Multiple Alignment in Computational Biology
  • 1.3 The Intractability of Multiple Sequence Alignment Problems
  • 1.4 Motivation and Goals of the Study
  • 1.5 Structure of the Paper

• Background on Sequence Alignment

  • 2.1 Basic Concepts in Sequence Alignment
  • 2.2 Pairwise Sequence Alignment: Dynamic Programming Algorithms
  • 2.3 Extensions to Multiple Sequence Alignment (MSA)
  • 2.4 The Role of Heuristics in Multiple Alignment
  • 2.5 Previous Work on the Complexity of MSA

• Intractability of Multiple Sequence Alignment

  • 3.1 The Computational Complexity of MSA
  • 3.2 NP-Hardness of Exact MSA Algorithms
  • 3.3 Approximation Algorithms and Their Limitations
  • 3.4 The Impact of Sequence Length and Number of Sequences
  • 3.5 Reductions to Known Hard Problems

• Theoretical Results on Multiple Alignment

  • 4.1 Formal Complexity Analysis of MSA
  • 4.2 Hardness Results for Special Cases of MSA
  • 4.3 Inapproximability Results: How Close Can We Get to Optimal Solutions?
  • 4.4 Lower Bounds and Upper Bounds in MSA Problems
  • 4.5 Exploration of Related Problems and Their Intractability

• Algorithms for Multiple Sequence Alignment

  • 5.1 Exact Algorithms for MSA
  • 5.2 Heuristic Methods for Approximation (e.g., Progressive Alignment, ClustalW)
  • 5.3 Metaheuristic Approaches (e.g., Genetic Algorithms, Simulated Annealing)
  • 5.4 Polynomial-Time Approximation Algorithms
  • 5.5 Complexity of Different Alignment Approaches

• Practical Implications and Applications of Intractability

  • 6.1 Aligning Large Genomic Datasets: Challenges in the Real World
  • 6.2 Practical Trade-offs Between Exact and Approximate Solutions
  • 6.3 Sequence Alignment in Phylogenetics and Evolutionary Biology
  • 6.4 Applications to Structural Biology and Drug Design
  • 6.5 Case Studies in Bioinformatics: Understanding Protein and DNA Sequences

• Experimental Evaluation and Benchmarking

  • 7.1 Performance Comparison of MSA Algorithms
  • 7.2 Computational Complexity in Practice: Empirical Results
  • 7.3 Benchmark Datasets and Experimental Setup
  • 7.4 Comparison of Exact vs. Heuristic Algorithms
  • 7.5 Scalability and Efficiency of Alignment Algorithms

• Advanced Topics in Multiple Sequence Alignment

  • 8.1 Aligning Sequences with Gaps and Indels
  • 8.2 Alignment in Non-Linear Spaces (e.g., Multiple Alignments with Structural Constraints)
  • 8.3 Phylogeny-Informed Sequence Alignment
  • 8.4 Alignment for Non-Biological Data (e.g., Strings, Graphs)
  • 8.5 Integrating Machine Learning Approaches for Alignment Improvement

• Open Problems and Future Directions

  • 9.1 Unsolved Problems in Multiple Sequence Alignment
  • 9.2 Theoretical Advances and Directions for Complexity Research
  • 9.3 New Approaches to Approximation and Heuristic Methods
  • 9.4 Exploring Parallel and Distributed Computing for Large-Scale Alignments
  • 9.5 Future Applications of MSA in Personalized Medicine and Genomics

• Conclusion

  • 10.1 Summary of Findings on the Intractability of MSA
  • 10.2 Implications for the Future of Computational Biology
  • 10.3 Final Thoughts on Advancing Sequence Alignment Research

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