Biological Data Mining

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Jake Y. Chen, Stefano Lonardi
CRC Press, Sep 1, 2009 - Computers - 733 pages
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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 interdisciplinary data mining researchers who cover state-of-the-art biological topics.

The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications.

This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.

 

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Contents

Chapter 1 Consensus Structure Prediction for RNA Alignments
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
Index
689
Back cover
715
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About the author (2009)

Jake Y. Chen is an assistant professor of informatics at Indiana University, an assistant professor of computer science at Purdue University, and director of the Indiana Center for Systems Biology and Personalized Medicine.

Stefano Lonardi is an associate professor of computer science and engineering at the University of California, Riverside.

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