chapter Part I -- chapter 1 2.1 Folding of a single RNA sequence -- chapter 1 3 Results -- chapter TABLE 1.1: -- chapter TABLE 1.2: -- chapter 2 1 Introduction -- 2.1.1 The dilemma of protein folding -- chapter 2 2.2 Segment approximation of secondary structure element (SSE) -- chapter 2 2.4 Building the hash table -- chapter TABLE 2.1: -- chapter TABLE 2.2: -- chapter TABLE 2.3: -- chapter TABLE 2.5: -- chapter 4 2 Background on Proteins -- chapter TABLE 4.2: -- chapter 4 6.2 Prediction accuracy -- chapter 5 4 General Object Analysis Methods -- 5.4.1 Global shape analysis -- chapter 5 4.2 Local shape analysis -- chapter TABLE 5.1: -- chapter References -- chapter TABLE 6.1: -- chapter 6 4.3 Protein structure and protein–protein interaction (PPI) network analysis by graph mining methods -- chapter 7 1 Introduction -- chapter 7 2.1 Support vector machines (SVMs) -- chapter 7 3 Residue-Wise Structure and Function Prediction -- chapter TABLE 7.2: -- chapter TABLE 7.3: -- chapter TABLE 7.4: -- chapter TABLE 7.5: -- chapter Part II -- chapter 8 2 Misassembly Signatures -- chapter 8 3 An entropy based sequence assembly validation -- chapter TABLE 8.1: -- chapter TABLE 8.2: -- chapter TABLE 8.3: -- chapter References -- chapter 9 1.1 Epistasis and common human diseases -- chapter TABLE 9.1: -- chapter 9 6 Summary -- chapter 10 2 Mapping of Expression Quantitative Trait Loci (eQTL) -- chapter References -- chapter 11 2 Current Gene Expression Microarray Preprocessing Steps and Methods—Affymetrix -- 11.2.1 Probe annotation -- chapter TABLE 11.1: -- chapter 12 3.5 Boosting -- chapter 13 1 Introduction -- chapter 13 4 External Indices -- chapter 13 4.3 The Fowlkes (F)-index -- chapter 13 5.1 Within cluster sum of squares (WCSS) -- chapter 13 5.2 KL: the Krzanowski and Lai index -- chapter TABLE 13.3: -- chapter TABLE 13.4: -- chapter TABLE 13.5: -- chapter TABLE 13.7: -- chapter TABLE 13.8: -- chapter TABLE 13.10: -- chapter TABLE 13.11: -- chapter 13 9 Conclusions and Open Problems -- chapter 14 4 Conclusions -- chapter Part III -- chapter 15 3 Computational Methods -- chapter References -- chapter 16 2 Framework for Regulatory Network Construction in Prokaryotes -- chapter 16 5.2 Motif prediction -- chapter 17 3 Network Properties -- chapter 17 5 Network Comparison and Alignment -- chapter TABLE 17.1: -- chapter References -- chapter 18 2.3 Betweenness -- chapter 18 3.3 Dynamic modules -- part Part IV -- chapter 19 1 Motivations for Mining Literature -- chapter TABLE 19.1: -- chapter TABLE 19.4: -- chapter TABLE 19.5: -- chapter References -- chapter TABLE 20.1: -- chapter TABLE 20.3: -- chapter TABLE 20.4: -- chapter TABLE 20.5: -- chapter TABLE 20.6: -- chapter TABLE 20.8: -- chapter TABLE 20.9: -- chapter TABLE 20.10: -- chapter TABLE 20.11: -- chapter 21 1.2 Data sharing standards -- chapter 21 2 Minimum Data Elements -- 21.2.1 The MIAME paradigm -- chapter TABLE 21.1: -- chapter 21 4.2 FuGE -- chapter References -- chapter 22 4.3 Modeling gene regulatory networks -- chapter TABLE 22.1: -- chapter TABLE 22.2: -- chapter TABLE 22.4: -- chapter TABLE 22.5: -- chapter Acknowledgments -- chapter Part V -- chapter 23 3 Data Mining Tools and Techniques for Identification of Molecular Biomarkers in Cancer -- chapter 23 5 Validation and Evaluation -- 23.5.1 Error rate -- chapter TABLE 24.1: -- chapter 24 2.1 Cancer biomarker prioritization -- chapter 25 2 Glycomics: Profiling Functional Carbohydrates Inside the Cell -- chapter TABLE 25.1: -- chapter Acknowledgment -- chapter 26 1 Introduction -- chapter 1 Consensus Structure Prediction for RNA Alignments / chapter TABLE 26.3: -- chapter 26 3 Unique Identifier.