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Applications of fuzzy logic in bioinformatics /
"Many biological systems and objects are intrinsically fuzzy as their properties and behaviors contain randomness or uncertainty. In addition, it has been shown that exact or optimal methods have significant limitation in many bioinformatics problems. Fuzzy set theory and fuzzy logic are ideal...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Hackensack, N.J. :
Imperial College Press ; Distributed by World Scientific,
2008.
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| Colección: | Series on advances in bioinformatics and computational biology ;
v. 9. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 1. Introduction to bioinformatics. 1.1. What is bioinformatics. 1.2. A brief history of bioinformatics. 1.3. Scope of bioinformatics. 1.4. Major challenges in bioinformatics. 1.5. Bioinformatics and computer science
- 2. Introduction to fuzzy set theory and fuzzy logic. 2.1. Where does fuzzy logic fit in computational science? 2.2. Why do we need to use fuzziness in biology? 2.3. Brief history of the field. 2.4. Fuzzy membership functions and operators. 2.5. Fuzzy relations and fuzzy logic inference. 2.6. Fuzzy clustering. 2.7. Fuzzy K-nearest neighbors. 2.8. Fuzzy measures and fuzzy integrals. 2.9. Summary and final thoughts
- 3. Fuzzy similarities in ontologies. 3.1. Introduction. 3.2. Definition of ontology-based similarity. 3.3. Set-based similarity measure. 3.4. Fuzzy measure similarity. 3.5. Fuzzy measure similarity for augmented sets of ontology objects. 3.6. Choquet fuzzy integral similarity measure. 3.7. Examples and applications of fuzzy measure similarity using GO terms. 3.8. Ontology similarity in data mining. 3.9. Discussion and summary
- 4. Fuzzy logic in structural bioinformatics. 4.1. Introduction. 4.2. Protein secondary structure prediction. 4.3. Protein solvent accessibility prediction. 4.4. Protein structure matching using fuzzy alignments. 4.5. Protein similarity calculation using fuzzy contact maps. 4.6. Protein structure class classification. 4.7. Summary
- 5. Application of fuzzy logic in microarray data analyses. 5.1. Introduction. 5.2. Clustering algorithms. 5.3. Inferring gene networks using fuzzy rule systems. 5.4. Discussion and summary
- 6. Other applications. 6.1. Overview. 6.2. Applications in biological sequence analyses. 6.3. Application in computational proteomics. 6.4. Application in drug design. 6.5. Discussion and summary
- 7. Summary and outlook.