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.