Metaheuristics for string problems in bio-informatics
This book will present the latest research on metaheuristic algorithms for some of the most important string problems in bio-informatics. Optimization problems related to strings-such as protein or DNA sequences-are very common in bioinformatics. Examples include string selection problems such as th...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, England ; Hoboken, New Jersey :
ISTE
2016.
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| Colección: | Computer engineering series (London, England)
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849078606719 |
Tabla de Contenidos:
- Cover ; Dedication; Title Page ; Copyright ; Contents; Preface; Acknowledgments; List of Acronyms; 1. Introduction; 1.1. Complete methods for combinatorial optimization; 1.1.1. Linear programming relaxation; 1.1.2. Cutting plane techniques; 1.1.3. General-purpose ILP solvers; 1.1.4. Dynamic programming; 1.2. Approximate methods: metaheuristics; 1.2.1. Ant colony optimization; 1.2.2. Evolutionary algorithms; 1.2.3. Greedy randomized adaptive search procedures; 1.2.4. Iterated local search; 1.2.5. Simulated annealing; 1.2.6. Other metaheuristics; 1.2.7. Hybrid approaches
- 1.3. Outline of the book2. Minimum Common String Partition Problem; 2.1. The MCSP problem; 2.1.1. Technical description of the UMCSP problem; 2.1.2. Literature review; 2.1.3. Organization of this chapter; 2.2. An ILP model for the UMCSP problem; 2.3. Greedy approach; 2.4. Construct, merge, solve and adapt; 2.5. Experimental evaluation; 2.5.1. Benchmarks; 2.5.2. Tuning CMSA; 2.5.3. Results; 2.6. Future work; 3. Longest Common Subsequence Problems; 3.1. Introduction; 3.1.1. LCS problems; 3.1.2. ILP models for LCS and RFLCS problems; 3.1.3. Organization of this chapter
- 3.2. Algorithms for the LCS problem 3.2.1. Beam search; 3.2.2. Upper bound; 3.2.3. Beam search framework; 3.2.4. Beam-ACO; 3.2.5. Experimental evaluation; 3.3. Algorithms for the RFLCS problem; 3.3.1. CMSA; 3.3.2. Experimental evaluation; 3.4. Future work; 4. The Most Strings With Few Bad Columns Problem; 4.1. The MSFBC problem; 4.1.1. Literature review; 4.2. An ILP model for the MSFBC problem; 4.3. Heuristic approaches; 4.3.1. Frequency-based greedy; 4.3.2. Truncated pilot method; 4.4. ILP-based large neighborhood search; 4.5. Experimental evaluation; 4.5.1. Benchmarks; 4.5.2. Tuning of LNS
- 4.5.3. Results4.6. Future work; 5. Consensus String Problems; 5.1. Introduction; 5.1.1. Creating diagnostic probes for bacterial infections; 5.1.2. Primer design; 5.1.3. Discovering potential drug targets; 5.1.4. Motif search; 5.2. Organization of this chapter; 5.3. The closest string problem and the close to most string problem; 5.3.1. ILP models for the CSP and the CTMSP; 5.3.2. Literature review; 5.3.3. Exact approaches for the CSP; 5.3.4. Approximation algorithms for the CSP; 5.3.5. Heuristics and metaheuristics for the CSP
- 5.4. The farthest string problem and the far from most string problem5.4.1. ILP models for the FSP and the FFMSP; 5.4.2. Literature review; 5.4.3. Heuristics and metaheuristics for the FFMSP; 5.5. An ILP-based heuristic; 5.6. Future work; 6. Alignment Problems; 6.1. Introduction; 6.1.1. Organization of this chapter; 6.2. The pairwise alignment problem; 6.2.1. Smith and Waterman's algorithm; 6.3. The multiple alignment problem; 6.3.1. Heuristics for the multiple alignment problem; 6.3.2. Metaheuristics for the multiple alignment problem; 6.4. Conclusion and future work; 7. Conclusions
- 7.1. DNA sequencing
