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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 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 validation.
- Provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems
- Uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems
- Contains 45 bioinformatics problems that have been investigated in recent research
Computer scientists who are interested in designing new data mining algorithms and biologists who are trying to solve bioinformatics problems using existing data mining tools.
- List of figures
- List of tables
- About the author
- 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
- 1.6 Model assessment
- 1.7 Model deployment
- 1.8 Summary
- 2: Introduction to bioinformatics
- 2.1 A primer to molecular biology
- 2.2 What is bioinformatics?
- 2.3 Data mining issues in bioinformatics
- 2.4 Challenges in biological data mining
- 2.5 Summary
- 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
- 3.6 Validation: Permutation p-value calculation
- 3.7 Discussion and future perspective
- 4: Phosphorylation site prediction
- 4.1 Background and problem description
- 4.2 Data collection and data preprocessing
- 4.3 Modeling: Different learning schemes
- 4.4 Validation: Cross-validation and independent test
- 4.5 Discussion and future perspective
- 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
- 5.6 Validation: Target-decoy versus decoy-free
- 5.7 Discussion and future perspective
- 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
- 6.5 Validation
- 6.6 Discussion and future perspective
- 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
- 7.4 Validation
- 7.5 Discussion and future perspective
- 8: Biomarker discovery
- 8.1 An introduction to biomarker discovery
- 8.2 Data preprocessing
- 8.3 Modeling
- 8.4 Validation
- 8.5 Case study
- 8.6 Discussion and future perspective
- No. of pages:
- © Woodhead Publishing 2015
- 4th June 2015
- Woodhead Publishing
- Hardcover ISBN:
- eBook ISBN:
Zengyou He is an Associate Professor at the School of Software, Dalian University of Technology, P.R China. He received his BS, MS, and PhD in Computer science from Harbin Institute of Technology, P.R China and was a Research associate in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology from 2007 to 2010. His research interests include Computational proteomics and Biological data mining. He has published more than 20 papers on leading journals in the field of bioinformatics, including Bioinformatics, BMC Bioinformatics, Briefings in Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics and Journal of Computational Biology.
Associate Professor, School of Software, Dalian University of Technology, China
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