Main 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.

Contents:

Chapter 1. Introduction. 1.1 Data Mining Algorithms. 1.2 Advances in Genomic Techniques. 1.3 Case Studies: Building Data Mining Algorithms for Genomic Applications. Chapter 2. Data Mining Algorithms. 2.1 Dimension Reduction and Transformation Algorithms. 2.2 Machine Learning Algorithms. 2.3 Clustering Algorithms. 2.4 Graph Algorithms. 2.5 Numerical Optimization Algorithms. Chapter 3. Advances in Genomic Experiment Techniques. 3.1 Single Nucleotide Polymorphisms (SNPs). 3.2 HapMap Project for Genomic Studies. 3.3 Haplotypes and Haplotype Blocks. 3.4 Genomic Analysis with Microarray Experiments. Chapter 4. Case Study I: Hierarchical Clustering and Graph Algorithms for Tag-SNP Selection. 4.1 Background. 4.2 CLUSTAG: Its Theory. 4.3 Experimental Results of CLUSTAG. 4.4 WCLUSTAG: Its Theory and Application for Functional and Linkage Disequilibrium Information. 4.5 WCLUSTAG Experimental Genomic Results. 4.6 Result Discussions Chapter 5. Case Study II: Constrained Unidimensional Scaling for Linkage Disequilibrium Maps. 5.1 Background. 5.2 Theoretical Background for Non-parametric LD Maps. 5.3 Applications of Non-parametric LD Maps in Genomics. 5.4 Development of Alterative Approach with Iterative Algorithms. 5.5 Remarks and Discussions. Chapter 6. Case Study III: Hybrid PCA-NN Algorithms for Continuous Microarry Time Series. 6.1 Background. 6.2 Motivations for the Hybrid PCA-NN Algorithms. 6.3 Data Description of Microarray Time Series Datasets. 6.4 Methods and Results. 6.5 Analysis on the Network Structure and the Out-of-Sample Validations. 6.6 Result Discussions. Chapter 7. Discussions and Future Data Mining Projects. 7.1 Tag-SNP Selection and Future Projects. 7.2 Algorithms for Non-Parametric LD Maps Constructions. 7.3 Hybrid Models for Continuous Microarray Time Series Analysis and Future Projects. Bibliography.