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...
Saved in:
| Other Authors: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
London : Hackensack, N.J. :
Imperial College Press ; Distributed by World Scientific,
2008.
|
| Series: | Series on advances in bioinformatics and computational biology ;
v. 9. |
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | "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 to describe some biological systems/objects and provide good tools for some bioinformatics problems. This book comprehensively addresses several important bioinformatics topics using fuzzy concepts and approaches, including measurement of ontological similarity, protein structure prediction/analysis, and microarray data analysis. It also reviews other bioinformatics applications using fuzzy techniques." "Readership: Postdoctoral fellows, students, senior investigators and professional practitioners/bioinformatics experts. Also used as a textbook for upper undergraduates and graduates in bioinformatics."--Jacket |
| Physical Description: | 1 online resource (xix, 225 pages) : illustrations |
| Bibliography: | Includes bibliographical references (pages 196-221) and index. |
| ISBN: | 9781848162594 1848162596 |
Staff View
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| 245 | 0 | 0 | |a Applications of fuzzy logic in bioinformatics / |c Dong Xu [and others]. |
| 260 | |a London : |b Imperial College Press ; |a Hackensack, N.J. : |b Distributed by World Scientific, |c 2008. | ||
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| 504 | |a Includes bibliographical references (pages 196-221) and index. | ||
| 520 | 1 | |a "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 to describe some biological systems/objects and provide good tools for some bioinformatics problems. This book comprehensively addresses several important bioinformatics topics using fuzzy concepts and approaches, including measurement of ontological similarity, protein structure prediction/analysis, and microarray data analysis. It also reviews other bioinformatics applications using fuzzy techniques." "Readership: Postdoctoral fellows, students, senior investigators and professional practitioners/bioinformatics experts. Also used as a textbook for upper undergraduates and graduates in bioinformatics."--Jacket | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 650 | 0 | |a Bioinformatics. |0 http://id.loc.gov/authorities/subjects/sh00003585 | |
| 650 | 0 | |a Computational biology. |0 http://id.loc.gov/authorities/subjects/sh2003008355 | |
| 650 | 0 | |a Fuzzy logic. |0 http://id.loc.gov/authorities/subjects/sh93006704 | |
| 650 | 0 | |a Fuzzy sets. |0 http://id.loc.gov/authorities/subjects/sh85052627 | |
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| contents | 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. |
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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 to describe some biological systems/objects and provide good tools for some bioinformatics problems. This book comprehensively addresses several important bioinformatics topics using fuzzy concepts and approaches, including measurement of ontological similarity, protein structure prediction/analysis, and microarray data analysis. It also reviews other bioinformatics applications using fuzzy techniques." "Readership: Postdoctoral fellows, students, senior investigators and professional practitioners/bioinformatics experts. Also used as a textbook for upper undergraduates and graduates in bioinformatics."--Jacket</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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. 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| genre | Electronic books. |
| genre_facet | Electronic books. |
| id | ZDB-4-EBA-ocn826660269 |
| illustrated | Illustrated |
| indexdate | 2025-04-11T08:41:14Z |
| institution | BVB |
| isbn | 9781848162594 1848162596 |
| language | English |
| oclc_num | 826660269 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (xix, 225 pages) : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Imperial College Press ; Distributed by World Scientific, |
| record_format | marc |
| series | Series on advances in bioinformatics and computational biology ; |
| series2 | Series on advances in bioinformatics and computational biology ; |
| spelling | Applications of fuzzy logic in bioinformatics / Dong Xu [and others]. London : Imperial College Press ; Hackensack, N.J. : Distributed by World Scientific, 2008. 1 online resource (xix, 225 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Bibliography Series on advances in bioinformatics and computational biology ; v. 9 Includes bibliographical references (pages 196-221) and index. "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 to describe some biological systems/objects and provide good tools for some bioinformatics problems. This book comprehensively addresses several important bioinformatics topics using fuzzy concepts and approaches, including measurement of ontological similarity, protein structure prediction/analysis, and microarray data analysis. It also reviews other bioinformatics applications using fuzzy techniques." "Readership: Postdoctoral fellows, students, senior investigators and professional practitioners/bioinformatics experts. Also used as a textbook for upper undergraduates and graduates in bioinformatics."--Jacket Print version record. 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. Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Computational biology. http://id.loc.gov/authorities/subjects/sh2003008355 Fuzzy logic. http://id.loc.gov/authorities/subjects/sh93006704 Fuzzy sets. http://id.loc.gov/authorities/subjects/sh85052627 Computational Biology Bio-informatique. Logique floue. Ensembles flous. COMPUTERS Bioinformatics. bisacsh Bioinformatics fast Computational biology fast Fuzzy logic fast Fuzzy sets fast Molecular biology. thema Science. ukslc Electronic books. Xu, Dong, 1965- https://id.oclc.org/worldcat/entity/E39PCjJJCRH8Cyrww97ggqKhf3 http://id.loc.gov/authorities/names/n2007014632 has work: Applications of fuzzy logic in bioinformatics (Text) https://id.oclc.org/worldcat/entity/E39PCFDr7xHkTb3WwGGFjxFD7b https://id.oclc.org/worldcat/ontology/hasWork Print version: Applications of fuzzy logic in bioinformatics. London : Imperial College Press ; Hackensack, N.J. Distributed by World Scientific, 2008 9781848162587 (DLC) 2009417103 (OCoLC)236323724 Series on advances in bioinformatics and computational biology ; v. 9. http://id.loc.gov/authorities/names/no2005039596 |
| spellingShingle | Applications of fuzzy logic in bioinformatics / Series on advances in bioinformatics and computational biology ; 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. Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Computational biology. http://id.loc.gov/authorities/subjects/sh2003008355 Fuzzy logic. http://id.loc.gov/authorities/subjects/sh93006704 Fuzzy sets. http://id.loc.gov/authorities/subjects/sh85052627 Computational Biology Bio-informatique. Logique floue. Ensembles flous. COMPUTERS Bioinformatics. bisacsh Bioinformatics fast Computational biology fast Fuzzy logic fast Fuzzy sets fast Molecular biology. thema Science. ukslc |
| subject_GND | http://id.loc.gov/authorities/subjects/sh00003585 http://id.loc.gov/authorities/subjects/sh2003008355 http://id.loc.gov/authorities/subjects/sh93006704 http://id.loc.gov/authorities/subjects/sh85052627 |
| title | Applications of fuzzy logic in bioinformatics / |
| title_auth | Applications of fuzzy logic in bioinformatics / |
| title_exact_search | Applications of fuzzy logic in bioinformatics / |
| title_full | Applications of fuzzy logic in bioinformatics / Dong Xu [and others]. |
| title_fullStr | Applications of fuzzy logic in bioinformatics / Dong Xu [and others]. |
| title_full_unstemmed | Applications of fuzzy logic in bioinformatics / Dong Xu [and others]. |
| title_short | Applications of fuzzy logic in bioinformatics / |
| title_sort | applications of fuzzy logic in bioinformatics |
| topic | Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Computational biology. http://id.loc.gov/authorities/subjects/sh2003008355 Fuzzy logic. http://id.loc.gov/authorities/subjects/sh93006704 Fuzzy sets. http://id.loc.gov/authorities/subjects/sh85052627 Computational Biology Bio-informatique. Logique floue. Ensembles flous. COMPUTERS Bioinformatics. bisacsh Bioinformatics fast Computational biology fast Fuzzy logic fast Fuzzy sets fast Molecular biology. thema Science. ukslc |
| topic_facet | Bioinformatics. Computational biology. Fuzzy logic. Fuzzy sets. Computational Biology Bio-informatique. Logique floue. Ensembles flous. COMPUTERS Bioinformatics. Bioinformatics Computational biology Fuzzy logic Fuzzy sets Molecular biology. Science. Electronic books. |
| work_keys_str_mv | AT xudong applicationsoffuzzylogicinbioinformatics |