Biological Data MiningJake Y. Chen, Stefano Lonardi Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplin |
Contents
| 3 | |
Chapter 2 Invariant Geometric Properties of Secondary Structure Elements in Proteins | 27 |
Chapter 3 Discovering 3D Motifs in RNA | 49 |
Chapter 4 Protein Structure Classification Using Machine Learning Methods | 69 |
New Approaches in Structural Proteomics | 89 |
Chapter 6 Advanced Graph Mining Methods for Protein Analysis | 111 |
Chapter 7 Predicting Local Structure and Function of Proteins | 137 |
Genomics Transcriptomics and Proteomics | 161 |
Chapter 16 Computational Methods for Unraveling Transcriptional Regulatory Networks in Prokaryotes | 377 |
Chapter 17 Computational Methods for Analyzing and Modeling Biological Networks | 397 |
Chapter 18 Statistical Analysis of Biomolecular Networks | 429 |
Literature Ontology and Knowledge Integration | 447 |
Literature Mining for Biomedical Knowledge Discovery | 449 |
Chapter 20 Mining Biological Interactions from Biomedical Texts for Efficient Query Answering | 485 |
Chapter 21 OntologyBased Knowledge Representation of Experiment Metadata in Biological Data Mining | 529 |
Chapter 22 Redescription Mining and Applications in Bioinformatics | 561 |
Chapter 8 Computational Approaches for Genome Assembly Validation | 163 |
Chapter 9 Mining Patterns of Epistasis in Human Genetics | 187 |
Chapter 10 Discovery of Regulatory Mechanisms from Gene Expression Variation by eQTL Analysis | 205 |
Chapter 11 Statistical Approaches to Gene Expression Microarray Data Preprocessing | 229 |
Chapter 12 Application of Feature Selection and Classification to Computational Molecular Biology | 257 |
Chapter 13 Statistical Indices for Computational and Data Driven Class Discovery in Microarray Data | 295 |
Chapter 14 Computational Approaches to Peptide Retention Time Prediction for Proteomics | 337 |
Functional and Molecular Interaction Networks | 351 |
Chapter 15 Inferring Protein Functional Linkage Based on Sequence Information and Beyond | 353 |
Genome Medicine Applications | 587 |
Chapter 23 Data Mining Tools and Techniques for Identification of Biomarkers for Cancer | 589 |
Assessing the in vivo Impact of in vitro Models by in silico Mining of Microarray Database Literature and Gene Annotation | 615 |
Chapter 25 Biomarker Discovery by Mining Glycomic and Lipidomic Data | 627 |
Chapter 26 Data Mining Chemical Structures and Biological Data | 649 |
| 689 | |
Back cover | 715 |
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Common terms and phrases
Affymetrix algorithm alignment amino acid analysis annotation applied approach array associated atoms base pair Bioinformatics biological relations biomarkers biomedical cancer cell chemical structure classification complex computational correlation data mining database dataset defined degree distribution descriptors detection discovery disease distance distribution domain edges entities epistasis eQTL evaluation example extraction Figure fold fragment function gene expression genetic genome glycan glycomics graph graph mining graphlets identify input interaction kernel lipidomics lipids machine learning matching matrix measure methods microarray microarray data modules molecular molecules motifs multiple nodes nucleotide number of clusters ontology operon parameters patterns peptide performance phylogenetic PPI networks prediction probe problem profiles protein structure protein surface proteomics query random redescription regulatory represent residue sample score secondary structure selection sequence similarity specific statistical subgraphs subset support vector machines Table techniques tion triplets types values vector yeast