Data Mining and Applications in Genomics

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.