Machine learning approaches to bioinformatics /:
This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies,...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; Hackensack, NJ :
World Scientific,
©2010.
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| Schriftenreihe: | Science, engineering, and biology informatics ;
v. 4. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes. An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects. |
| Beschreibung: | 1 online resource (xiv, 322 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789814287319 9814287318 |
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| 245 | 1 | 0 | |a Machine learning approaches to bioinformatics / |c Zheng Rong Yang. |
| 260 | |a Singapore ; |a Hackensack, NJ : |b World Scientific, |c ©2010. | ||
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| 490 | 1 | |a Science, engineering, and biology informatics ; |v v. 4 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 1. Introduction. 1.1. Brief history of bioinformatics. 1.2. Database application in bioinformatics. 1.3. Web tools and services for sequence homology alignment. 1.4. Pattern analysis. 1.5. The contribution of information technology. 1.6. Chapters -- 2. Introduction to unsupervised learning -- 3. Probability density estimation approaches. 3.1. Histogram approach. 3.2. Parametric approach. 3.3. Non-parametric approach -- 4. Dimension reduction. 4.1. General. 4.2. Principal component analysis. 4.3. An application of PCA. 4.4. Multi-dimensional scaling. 4.5. Application of the Sammon algorithm to gene data -- 5. Cluster analysis. 5.1. Hierarchical clustering. 5.2. K-means. 5.3. Fuzzy C-means. 5.4. Gaussian mixture models. 5.5. Application of clustering algorithms to the Burkholderia pseudomallei gene expression data -- 6. Self-organising map. 6.1. Vector quantization. 6.2. SOM structure. 6.3. SOM learning algorithm. 6.4. Using SOM for classification. 6.5. Bioinformatics applications of VQ and SOM. 6.6. A case study of gene expression data analysis. 6.7. A case study of sequence data analysis -- 7. Introduction to supervised learning. 7.1. General concepts. 7.2. General definition. 7.3. Model evaluation. 7.4. Data organisation. 7.5. Bayes rule for classification -- 8. Linear/quadratic discriminant analysis and K-nearest neighbour. 8.1. Linear discriminant analysis. 8.2. Generalised discriminant analysis. 8.3. K-nearest neighbour. 8.4. KNN for gene data analysis -- 9. Classification and regression trees, random forest algorithm. 9.1. Introduction. 9.2. Basic principle for constructing a classification tree. 9.3. Classification and regression tree. 9.4. CART for compound pathway involvement prediction. 9.5. The random forest algorithm. 9.6. RF for analyzing Burkholderia pseudomallei gene expression profiles -- 10. Multi-layer perceptron. 10.1. Introduction. 10.2. Learning theory. 10.3. Learning algorithms. 10.4. Applications to bioinformatics. 10.5. A case study on Burkholderia pseudomallei gene expression data -- 11. Basis function approach and vector machines. 11.1. Introduction. 11.2. Radial-basis function neural network (RBFNN). 11.3. Bio-basis function neural network. 11.4. Support vector machine. 11.5. Relevance vector machine -- 12. Hidden Markov model. 12.1. Markov model. 12.2. Hidden Markov model. 12.3. HMM for sequence classification -- 13. Feature selection. 13.1. Built-in strategy. 13.2. Exhaustive strategy. 13.3. Heuristic strategy -- orthogonal least square approach. 13.4. Criteria for feature selection -- 14. Feature extraction (biological data coding). 14.1. Molecular sequences. 14.2. Chemical compounds. 14.3. General definition. 14.4. Sequence analysis -- 15. Sequence/structural bioinformatics foundation -- peptide classification. 15.1. Nitration site prediction. 15.2. Plant promoter region prediction -- 16. Gene network -- causal network and Bayesian networks. 16.1. Gene regulatory network. 16.2. Causal networks, networks, graphs. 16.3. A brief review of the probability. 16.4. Discrete Bayesian network. 16.5. Inference with discrete Bayesian network. 16.6. Learning discrete Bayesian network. 16.7. Bayesian networks for gene regulartory networks. 16.8. Bayesian networks for discovering peptide patterns. 16.9. Bayesian networks for analysing Burkholderia pseudomallei gene data -- 17. S-systems. 17.1. Michealis-Menten change law. 17.2. S-system. 17.3. Simplification of an S-system. 17.4. Approaches for structure identification and parameter estimation. 17.5. Steady-state analysis of an S-system. 17.6. Sensitivity of an S-system -- 18. Future directions. 18.1. Multi-source data. 18.2. Gene regulatory network construction. 18.3. Building models using incomplete data. 18.4. Biomarker detection from gene expression data. | |
| 520 | |a This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes. An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects. | ||
| 588 | 0 | |a Print version record. | |
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| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Bioinformatics |v Case studies. | |
| 650 | 0 | |a Machine learning |v Case studies. | |
| 650 | 0 | |a Computational biology. |0 http://id.loc.gov/authorities/subjects/sh2003008355 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 2 | |a Computational Biology |0 https://id.nlm.nih.gov/mesh/D019295 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Bio-informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Bio-informatique |v Études de cas. | |
| 650 | 6 | |a Apprentissage automatique |v Études de cas. | |
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| contents | 1. Introduction. 1.1. Brief history of bioinformatics. 1.2. Database application in bioinformatics. 1.3. Web tools and services for sequence homology alignment. 1.4. Pattern analysis. 1.5. The contribution of information technology. 1.6. Chapters -- 2. Introduction to unsupervised learning -- 3. Probability density estimation approaches. 3.1. Histogram approach. 3.2. Parametric approach. 3.3. Non-parametric approach -- 4. Dimension reduction. 4.1. General. 4.2. Principal component analysis. 4.3. An application of PCA. 4.4. Multi-dimensional scaling. 4.5. Application of the Sammon algorithm to gene data -- 5. Cluster analysis. 5.1. Hierarchical clustering. 5.2. K-means. 5.3. Fuzzy C-means. 5.4. Gaussian mixture models. 5.5. Application of clustering algorithms to the Burkholderia pseudomallei gene expression data -- 6. Self-organising map. 6.1. Vector quantization. 6.2. SOM structure. 6.3. SOM learning algorithm. 6.4. Using SOM for classification. 6.5. Bioinformatics applications of VQ and SOM. 6.6. A case study of gene expression data analysis. 6.7. A case study of sequence data analysis -- 7. Introduction to supervised learning. 7.1. General concepts. 7.2. General definition. 7.3. Model evaluation. 7.4. Data organisation. 7.5. Bayes rule for classification -- 8. Linear/quadratic discriminant analysis and K-nearest neighbour. 8.1. Linear discriminant analysis. 8.2. Generalised discriminant analysis. 8.3. K-nearest neighbour. 8.4. KNN for gene data analysis -- 9. Classification and regression trees, random forest algorithm. 9.1. Introduction. 9.2. Basic principle for constructing a classification tree. 9.3. Classification and regression tree. 9.4. CART for compound pathway involvement prediction. 9.5. The random forest algorithm. 9.6. RF for analyzing Burkholderia pseudomallei gene expression profiles -- 10. Multi-layer perceptron. 10.1. Introduction. 10.2. Learning theory. 10.3. Learning algorithms. 10.4. Applications to bioinformatics. 10.5. A case study on Burkholderia pseudomallei gene expression data -- 11. Basis function approach and vector machines. 11.1. Introduction. 11.2. Radial-basis function neural network (RBFNN). 11.3. Bio-basis function neural network. 11.4. Support vector machine. 11.5. Relevance vector machine -- 12. Hidden Markov model. 12.1. Markov model. 12.2. Hidden Markov model. 12.3. HMM for sequence classification -- 13. Feature selection. 13.1. Built-in strategy. 13.2. Exhaustive strategy. 13.3. Heuristic strategy -- orthogonal least square approach. 13.4. Criteria for feature selection -- 14. Feature extraction (biological data coding). 14.1. Molecular sequences. 14.2. Chemical compounds. 14.3. General definition. 14.4. Sequence analysis -- 15. Sequence/structural bioinformatics foundation -- peptide classification. 15.1. Nitration site prediction. 15.2. Plant promoter region prediction -- 16. Gene network -- causal network and Bayesian networks. 16.1. Gene regulatory network. 16.2. Causal networks, networks, graphs. 16.3. A brief review of the probability. 16.4. Discrete Bayesian network. 16.5. Inference with discrete Bayesian network. 16.6. Learning discrete Bayesian network. 16.7. Bayesian networks for gene regulartory networks. 16.8. Bayesian networks for discovering peptide patterns. 16.9. Bayesian networks for analysing Burkholderia pseudomallei gene data -- 17. S-systems. 17.1. Michealis-Menten change law. 17.2. S-system. 17.3. Simplification of an S-system. 17.4. Approaches for structure identification and parameter estimation. 17.5. Steady-state analysis of an S-system. 17.6. Sensitivity of an S-system -- 18. Future directions. 18.1. Multi-source data. 18.2. Gene regulatory network construction. 18.3. Building models using incomplete data. 18.4. Biomarker detection from gene expression data. |
| ctrlnum | (OCoLC)670430124 |
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index.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">1. Introduction. 1.1. Brief history of bioinformatics. 1.2. Database application in bioinformatics. 1.3. Web tools and services for sequence homology alignment. 1.4. Pattern analysis. 1.5. The contribution of information technology. 1.6. Chapters -- 2. Introduction to unsupervised learning -- 3. Probability density estimation approaches. 3.1. Histogram approach. 3.2. Parametric approach. 3.3. Non-parametric approach -- 4. Dimension reduction. 4.1. General. 4.2. Principal component analysis. 4.3. An application of PCA. 4.4. Multi-dimensional scaling. 4.5. Application of the Sammon algorithm to gene data -- 5. Cluster analysis. 5.1. Hierarchical clustering. 5.2. K-means. 5.3. Fuzzy C-means. 5.4. Gaussian mixture models. 5.5. Application of clustering algorithms to the Burkholderia pseudomallei gene expression data -- 6. Self-organising map. 6.1. Vector quantization. 6.2. SOM structure. 6.3. SOM learning algorithm. 6.4. Using SOM for classification. 6.5. Bioinformatics applications of VQ and SOM. 6.6. A case study of gene expression data analysis. 6.7. A case study of sequence data analysis -- 7. Introduction to supervised learning. 7.1. General concepts. 7.2. General definition. 7.3. Model evaluation. 7.4. Data organisation. 7.5. Bayes rule for classification -- 8. Linear/quadratic discriminant analysis and K-nearest neighbour. 8.1. Linear discriminant analysis. 8.2. Generalised discriminant analysis. 8.3. K-nearest neighbour. 8.4. KNN for gene data analysis -- 9. Classification and regression trees, random forest algorithm. 9.1. Introduction. 9.2. Basic principle for constructing a classification tree. 9.3. Classification and regression tree. 9.4. CART for compound pathway involvement prediction. 9.5. The random forest algorithm. 9.6. RF for analyzing Burkholderia pseudomallei gene expression profiles -- 10. Multi-layer perceptron. 10.1. Introduction. 10.2. Learning theory. 10.3. Learning algorithms. 10.4. Applications to bioinformatics. 10.5. A case study on Burkholderia pseudomallei gene expression data -- 11. Basis function approach and vector machines. 11.1. Introduction. 11.2. Radial-basis function neural network (RBFNN). 11.3. Bio-basis function neural network. 11.4. Support vector machine. 11.5. Relevance vector machine -- 12. Hidden Markov model. 12.1. Markov model. 12.2. Hidden Markov model. 12.3. HMM for sequence classification -- 13. Feature selection. 13.1. Built-in strategy. 13.2. Exhaustive strategy. 13.3. Heuristic strategy -- orthogonal least square approach. 13.4. Criteria for feature selection -- 14. Feature extraction (biological data coding). 14.1. Molecular sequences. 14.2. Chemical compounds. 14.3. General definition. 14.4. Sequence analysis -- 15. Sequence/structural bioinformatics foundation -- peptide classification. 15.1. Nitration site prediction. 15.2. Plant promoter region prediction -- 16. Gene network -- causal network and Bayesian networks. 16.1. Gene regulatory network. 16.2. Causal networks, networks, graphs. 16.3. A brief review of the probability. 16.4. Discrete Bayesian network. 16.5. Inference with discrete Bayesian network. 16.6. Learning discrete Bayesian network. 16.7. Bayesian networks for gene regulartory networks. 16.8. Bayesian networks for discovering peptide patterns. 16.9. Bayesian networks for analysing Burkholderia pseudomallei gene data -- 17. S-systems. 17.1. Michealis-Menten change law. 17.2. S-system. 17.3. Simplification of an S-system. 17.4. Approaches for structure identification and parameter estimation. 17.5. Steady-state analysis of an S-system. 17.6. Sensitivity of an S-system -- 18. Future directions. 18.1. Multi-source data. 18.2. Gene regulatory network construction. 18.3. Building models using incomplete data. 18.4. 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| genre | Case studies fast |
| genre_facet | Case studies |
| id | ZDB-4-EBA-ocn670430124 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:17:34Z |
| institution | BVB |
| isbn | 9789814287319 9814287318 |
| language | English |
| oclc_num | 670430124 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xiv, 322 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | World Scientific, |
| record_format | marc |
| series | Science, engineering, and biology informatics ; |
| series2 | Science, engineering, and biology informatics ; |
| spelling | Yang, Zheng Rong. Machine learning approaches to bioinformatics / Zheng Rong Yang. Singapore ; Hackensack, NJ : World Scientific, ©2010. 1 online resource (xiv, 322 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Science, engineering, and biology informatics ; v. 4 Includes bibliographical references and index. 1. Introduction. 1.1. Brief history of bioinformatics. 1.2. Database application in bioinformatics. 1.3. Web tools and services for sequence homology alignment. 1.4. Pattern analysis. 1.5. The contribution of information technology. 1.6. Chapters -- 2. Introduction to unsupervised learning -- 3. Probability density estimation approaches. 3.1. Histogram approach. 3.2. Parametric approach. 3.3. Non-parametric approach -- 4. Dimension reduction. 4.1. General. 4.2. Principal component analysis. 4.3. An application of PCA. 4.4. Multi-dimensional scaling. 4.5. Application of the Sammon algorithm to gene data -- 5. Cluster analysis. 5.1. Hierarchical clustering. 5.2. K-means. 5.3. Fuzzy C-means. 5.4. Gaussian mixture models. 5.5. Application of clustering algorithms to the Burkholderia pseudomallei gene expression data -- 6. Self-organising map. 6.1. Vector quantization. 6.2. SOM structure. 6.3. SOM learning algorithm. 6.4. Using SOM for classification. 6.5. Bioinformatics applications of VQ and SOM. 6.6. A case study of gene expression data analysis. 6.7. A case study of sequence data analysis -- 7. Introduction to supervised learning. 7.1. General concepts. 7.2. General definition. 7.3. Model evaluation. 7.4. Data organisation. 7.5. Bayes rule for classification -- 8. Linear/quadratic discriminant analysis and K-nearest neighbour. 8.1. Linear discriminant analysis. 8.2. Generalised discriminant analysis. 8.3. K-nearest neighbour. 8.4. KNN for gene data analysis -- 9. Classification and regression trees, random forest algorithm. 9.1. Introduction. 9.2. Basic principle for constructing a classification tree. 9.3. Classification and regression tree. 9.4. CART for compound pathway involvement prediction. 9.5. The random forest algorithm. 9.6. RF for analyzing Burkholderia pseudomallei gene expression profiles -- 10. Multi-layer perceptron. 10.1. Introduction. 10.2. Learning theory. 10.3. Learning algorithms. 10.4. Applications to bioinformatics. 10.5. A case study on Burkholderia pseudomallei gene expression data -- 11. Basis function approach and vector machines. 11.1. Introduction. 11.2. Radial-basis function neural network (RBFNN). 11.3. Bio-basis function neural network. 11.4. Support vector machine. 11.5. Relevance vector machine -- 12. Hidden Markov model. 12.1. Markov model. 12.2. Hidden Markov model. 12.3. HMM for sequence classification -- 13. Feature selection. 13.1. Built-in strategy. 13.2. Exhaustive strategy. 13.3. Heuristic strategy -- orthogonal least square approach. 13.4. Criteria for feature selection -- 14. Feature extraction (biological data coding). 14.1. Molecular sequences. 14.2. Chemical compounds. 14.3. General definition. 14.4. Sequence analysis -- 15. Sequence/structural bioinformatics foundation -- peptide classification. 15.1. Nitration site prediction. 15.2. Plant promoter region prediction -- 16. Gene network -- causal network and Bayesian networks. 16.1. Gene regulatory network. 16.2. Causal networks, networks, graphs. 16.3. A brief review of the probability. 16.4. Discrete Bayesian network. 16.5. Inference with discrete Bayesian network. 16.6. Learning discrete Bayesian network. 16.7. Bayesian networks for gene regulartory networks. 16.8. Bayesian networks for discovering peptide patterns. 16.9. Bayesian networks for analysing Burkholderia pseudomallei gene data -- 17. S-systems. 17.1. Michealis-Menten change law. 17.2. S-system. 17.3. Simplification of an S-system. 17.4. Approaches for structure identification and parameter estimation. 17.5. Steady-state analysis of an S-system. 17.6. Sensitivity of an S-system -- 18. Future directions. 18.1. Multi-source data. 18.2. Gene regulatory network construction. 18.3. Building models using incomplete data. 18.4. Biomarker detection from gene expression data. This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes. An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects. Print version record. Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Bioinformatics Case studies. Machine learning Case studies. Computational biology. http://id.loc.gov/authorities/subjects/sh2003008355 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Computational Biology https://id.nlm.nih.gov/mesh/D019295 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Bio-informatique. Apprentissage automatique. Bio-informatique Études de cas. Apprentissage automatique Études de cas. Intelligence artificielle. artificial intelligence. aat COMPUTERS Bioinformatics. bisacsh Computational biology fast Artificial intelligence fast Bioinformatics fast Machine learning fast Case studies fast has work: Machine learning approaches to bioinformatics (Text) https://id.oclc.org/worldcat/entity/E39PCH63wg3JXMgBD9q6Trhrbd https://id.oclc.org/worldcat/ontology/hasWork Print version: Yang, Zheng Rong. Machine learning approaches to bioinformatics. Singapore ; Hackensack, NJ : World Scientific, ©2010 9789814287302 (OCoLC)619946410 Science, engineering, and biology informatics ; v. 4. http://id.loc.gov/authorities/names/no2007052160 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=340803 Volltext |
| spellingShingle | Yang, Zheng Rong Machine learning approaches to bioinformatics / Science, engineering, and biology informatics ; 1. Introduction. 1.1. Brief history of bioinformatics. 1.2. Database application in bioinformatics. 1.3. Web tools and services for sequence homology alignment. 1.4. Pattern analysis. 1.5. The contribution of information technology. 1.6. Chapters -- 2. Introduction to unsupervised learning -- 3. Probability density estimation approaches. 3.1. Histogram approach. 3.2. Parametric approach. 3.3. Non-parametric approach -- 4. Dimension reduction. 4.1. General. 4.2. Principal component analysis. 4.3. An application of PCA. 4.4. Multi-dimensional scaling. 4.5. Application of the Sammon algorithm to gene data -- 5. Cluster analysis. 5.1. Hierarchical clustering. 5.2. K-means. 5.3. Fuzzy C-means. 5.4. Gaussian mixture models. 5.5. Application of clustering algorithms to the Burkholderia pseudomallei gene expression data -- 6. Self-organising map. 6.1. Vector quantization. 6.2. SOM structure. 6.3. SOM learning algorithm. 6.4. Using SOM for classification. 6.5. Bioinformatics applications of VQ and SOM. 6.6. A case study of gene expression data analysis. 6.7. A case study of sequence data analysis -- 7. Introduction to supervised learning. 7.1. General concepts. 7.2. General definition. 7.3. Model evaluation. 7.4. Data organisation. 7.5. Bayes rule for classification -- 8. Linear/quadratic discriminant analysis and K-nearest neighbour. 8.1. Linear discriminant analysis. 8.2. Generalised discriminant analysis. 8.3. K-nearest neighbour. 8.4. KNN for gene data analysis -- 9. Classification and regression trees, random forest algorithm. 9.1. Introduction. 9.2. Basic principle for constructing a classification tree. 9.3. Classification and regression tree. 9.4. CART for compound pathway involvement prediction. 9.5. The random forest algorithm. 9.6. RF for analyzing Burkholderia pseudomallei gene expression profiles -- 10. Multi-layer perceptron. 10.1. Introduction. 10.2. Learning theory. 10.3. Learning algorithms. 10.4. Applications to bioinformatics. 10.5. A case study on Burkholderia pseudomallei gene expression data -- 11. Basis function approach and vector machines. 11.1. Introduction. 11.2. Radial-basis function neural network (RBFNN). 11.3. Bio-basis function neural network. 11.4. Support vector machine. 11.5. Relevance vector machine -- 12. Hidden Markov model. 12.1. Markov model. 12.2. Hidden Markov model. 12.3. HMM for sequence classification -- 13. Feature selection. 13.1. Built-in strategy. 13.2. Exhaustive strategy. 13.3. Heuristic strategy -- orthogonal least square approach. 13.4. Criteria for feature selection -- 14. Feature extraction (biological data coding). 14.1. Molecular sequences. 14.2. Chemical compounds. 14.3. General definition. 14.4. Sequence analysis -- 15. Sequence/structural bioinformatics foundation -- peptide classification. 15.1. Nitration site prediction. 15.2. Plant promoter region prediction -- 16. Gene network -- causal network and Bayesian networks. 16.1. Gene regulatory network. 16.2. Causal networks, networks, graphs. 16.3. A brief review of the probability. 16.4. Discrete Bayesian network. 16.5. Inference with discrete Bayesian network. 16.6. Learning discrete Bayesian network. 16.7. Bayesian networks for gene regulartory networks. 16.8. Bayesian networks for discovering peptide patterns. 16.9. Bayesian networks for analysing Burkholderia pseudomallei gene data -- 17. S-systems. 17.1. Michealis-Menten change law. 17.2. S-system. 17.3. Simplification of an S-system. 17.4. Approaches for structure identification and parameter estimation. 17.5. Steady-state analysis of an S-system. 17.6. Sensitivity of an S-system -- 18. Future directions. 18.1. Multi-source data. 18.2. Gene regulatory network construction. 18.3. Building models using incomplete data. 18.4. Biomarker detection from gene expression data. Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Bioinformatics Case studies. Machine learning Case studies. Computational biology. http://id.loc.gov/authorities/subjects/sh2003008355 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Computational Biology https://id.nlm.nih.gov/mesh/D019295 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Bio-informatique. Apprentissage automatique. Bio-informatique Études de cas. Apprentissage automatique Études de cas. Intelligence artificielle. artificial intelligence. aat COMPUTERS Bioinformatics. bisacsh Computational biology fast Artificial intelligence fast Bioinformatics fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh00003585 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2003008355 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D019295 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Machine learning approaches to bioinformatics / |
| title_auth | Machine learning approaches to bioinformatics / |
| title_exact_search | Machine learning approaches to bioinformatics / |
| title_full | Machine learning approaches to bioinformatics / Zheng Rong Yang. |
| title_fullStr | Machine learning approaches to bioinformatics / Zheng Rong Yang. |
| title_full_unstemmed | Machine learning approaches to bioinformatics / Zheng Rong Yang. |
| title_short | Machine learning approaches to bioinformatics / |
| title_sort | machine learning approaches to bioinformatics |
| topic | Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Bioinformatics Case studies. Machine learning Case studies. Computational biology. http://id.loc.gov/authorities/subjects/sh2003008355 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Computational Biology https://id.nlm.nih.gov/mesh/D019295 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Bio-informatique. Apprentissage automatique. Bio-informatique Études de cas. Apprentissage automatique Études de cas. Intelligence artificielle. artificial intelligence. aat COMPUTERS Bioinformatics. bisacsh Computational biology fast Artificial intelligence fast Bioinformatics fast Machine learning fast |
| topic_facet | Bioinformatics. Machine learning. Bioinformatics Case studies. Machine learning Case studies. Computational biology. Artificial intelligence. Computational Biology Artificial Intelligence Machine Learning Bio-informatique. Apprentissage automatique. Bio-informatique Études de cas. Apprentissage automatique Études de cas. Intelligence artificielle. artificial intelligence. COMPUTERS Bioinformatics. Computational biology Artificial intelligence Bioinformatics Machine learning Case studies |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=340803 |
| work_keys_str_mv | AT yangzhengrong machinelearningapproachestobioinformatics |