- Full Description
Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serves as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics.
- Table of Contents
Table of Contents
- Chapter 1. Introduction. Chapter 2. Data Mining Algorithms. Chapter 3. Advances in Genomic Experiment Techniques. Chapter 4. Case Study I: Hierarchical Clustering and Graph Algorithms for Tag
- SNP Selection. Chapter 5. Case Study II: Constrained Unidimensional Scaling for Linkage Disequilibrium Maps. Chapter 6. Case Study III: Hybrid PCA
- NN Algorithms for Continuous Microarry Time Series. Chapter 7. Discussions and Future Data Mining Projects. Bibliography.
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