| Description |
1 online resource (xx, 713 pages) : illustrations |
| Series |
Chapman & Hall/CRC data mining and knowledge discovery series |
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Chapman & Hall/CRC data mining and knowledge discovery series.
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| Contents |
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 / Junilda Spirollari -- chapter TABLE 26.3: -- chapter 26 3 Unique Identifier |
| Summary |
Presents comprehensive data mining concepts, theories and applications in biological and medical research. This book discusses challenge and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. It describes the relationships between data mining and related areas of computing |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
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Print version record |
| Subject |
Bioinformatics.
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Data mining.
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Computational biology.
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NATURE -- Reference.
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SCIENCE -- Life Sciences -- General.
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SCIENCE -- Life Sciences -- Biology.
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Bioinformatics
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Computational biology
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Data mining
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| Form |
Electronic book
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| Author |
Chen, Jake.
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Lonardi, Stefano.
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| ISBN |
9781420086850 |
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1420086855 |
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0429138709 |
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9780429138706 |
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