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Machine learning approaches to bioinformatics [electronic resource] / Zheng Rong Yang.

By: Yang, Zheng Rong.
Material type: TextTextSeries: Science, engineering, and biology informatics: v. 4.Publisher: Singapore ; Hackensack, NJ : World Scientific, c2010Description: 1 online resource (xiv, 322 p.) : ill.ISBN: 9789814287319 (electronic bk.); 9814287318 (electronic bk.).Subject(s): Bioinformatics | Machine learning | Bioinformatics -- Case studies | Machine learning -- Case studies | Natural history | Science | COMPUTERS -- BioinformaticsGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: Machine learning approaches to bioinformatics.DDC classification: 572.80285518 Online resources: EBSCOhost
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.
Summary: 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.
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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.

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Machine learning approaches to bioinformatics by Yang, Zheng Rong. ©2010
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