Data mining for bioinformatics applications /

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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 i...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Zengyou, He (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, UK : Woodhead Publishing is an imprint of Elsevier, [2015]
Colección:Woodhead Publishing series in biomedicine ; no. 76.
Temas:
Bioinformatics.
Data mining.
Computational Biology
Data Mining
Bio-informatique.
Exploration de donn�ees (Informatique)
COMPUTERS > Bioinformatics.
SCIENCE > Life Sciences > Biochemistry.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • Chapter 2: Introduction to bioinformatics
  • 2.1. 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
  • 2.4. Challenges in biological data mining
  • 2.5. 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
  • 4.2.1. Training data construction.
  • 4.2.2. 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
  • 5.5.2. A regression approach
  • 5.5.3. 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
  • 6.5. Validation
  • 6.6. Discussion and future perspective
  • References
  • Chapter 7: Protein complex identification from AP-MS data
  • 7.1. An introduction to protein complex identification
  • 7.2. Data collection and data preprocessing
  • 7.3. Modeling: A graph clustering framework
  • 7.3.1. The clique percolation approach
  • 7.3.2. The statistical inference method
  • 7.4. Validation
  • 7.5. Discussion and future perspective
  • References
  • Chapter 8: Biomarker discovery
  • 8.1. An introduction to biomarker discovery
  • 8.2. Data preprocessing
  • 8.3. Modeling
  • 8.3.1. Cut point selection
  • 8.3.2. Binary threshold classifier
  • 8.3.3. Feature evaluation criterion.
  • 8.4. Validation
  • 8.5. Case study
  • 8.6. Discussion and future perspective
  • References
  • Conclusions
  • Index.

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