UID:
edoccha_9960074147802883
Format:
1 online resource (100 p.)
Edition:
1st ed.
ISBN:
0-08-100107-X
Series Statement:
Woodhead Publishing Series in Biomedicine ; Number 76
Content:
Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validat
Note:
Description based upon print version of record.
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Front Cover; Data Mining for Bioinformatics Applications; Copyright; Contents; List of figures; List of tables; About the author; Dedication; Introduction; Audience; Acknowledgments; Chapter 1: An overview of data mining; 1.1. What's data mining?; 1.2. Data mining process models; 1.3. Data collection; 1.4. Data preprocessing; 1.5. Data modeling; 1.5.1. Pattern mining; 1.5.2. Supervised predictive modeling: Classification and regression; 1.5.3. Unsupervised descriptive modeling: Cluster analysis; 1.6. Model assessment; 1.7. Model deployment; 1.8. Summary; References
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Chapter 2: Introduction to bioinformatics2.1. A primer to molecular biology; 2.2. What is bioinformatics?; 2.3. Data mining issues in bioinformatics; 2.3.1. Sequences; 2.3.1.1. The analysis and comparison of multiple sequences; 2.3.1.2. Sequence identification from experimental data; 2.3.1.3. Sequence classification and regression; 2.3.2. Structures; 2.3.2.1. Multiple structure analysis; 2.3.2.2. Structure prediction; 2.3.2.3. Structure-based prediction; 2.3.3. Networks; 2.3.3.1. Network analysis; 2.3.3.2. Network inference; 2.3.3.3. Network-assisted prediction
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2.4. Challenges in biological data mining2.5. Summary; References; Chapter 3: Phosphorylation motif discovery; 3.1. Background and problem description; 3.2. The nature of the problem; 3.3. Data collection; 3.4. Data preprocessing; 3.5. Modeling: A discriminative pattern mining perspective; 3.5.1. The Motif-All algorithm; 3.5.2. The C-Motif algorithm; 3.6. Validation: Permutation p-value calculation; 3.7. Discussion and future perspective; References; Chapter 4: Phosphorylation site prediction; 4.1. Background and problem description; 4.2. Data collection and data preprocessing
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4.2.1. Training data construction4.2.2. Feature extraction; 4.3. Modeling: Different learning schemes; 4.3.1. Standard supervised learning; 4.3.2. Active learning; 4.3.3. Transfer learning; 4.4. Validation: Cross-validation and independent test; 4.5. Discussion and future perspective; References; Chapter 5: Protein inference in shotgun proteomics; 5.1. Introduction to proteomics; 5.2. Protein identification in proteomics; 5.3. Protein inference: Problem formulation; 5.4. Data collection; 5.5. Modeling with different data mining techniques; 5.5.1. A classification approach
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5.5.2. A regression approach5.5.3. A clustering approach; 5.6. Validation: Target-decoy versus decoy-free; 5.6.1. Target-decoy method; 5.6.2. Decoy-free method; 5.6.3. On unbiased performance evaluation for protein inference; 5.7. Discussion and future perspective; References; Chapter 6: PPI network inference from AP-MS data; 6.1. Introduction to protein-protein interactions; 6.2. AP-MS data generation; 6.3. Data collection and preprocessing; 6.4. Modeling with different data mining techniques; 6.4.1. A correlation mining approach; 6.4.2. A discriminative pattern mining approach
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6.5. Validation
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English
Additional Edition:
ISBN 0-08-100100-2
Language:
English
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