Advanced Data Mining Technologies in Bioinformatics

Pirmais vāks
Hui-Huang Hsu
Idea Group Inc (IGI), 2006. gada 1. janv. - 329 lappuses
The technologies in data mining have been applied to bioinformatics research in the past few years with success, but more research in this field is necessary. While tremendous progress has been made over the years, many of the fundamental challenges in bioinformatics are still open. Data mining plays a essential role in understanding the emerging problems in genomics, proteomics, and systems biology.
Advanced Data Mining Technologies in Bioinformatics covers important research topics of data mining on bioinformatics. Readers of this book will gain an understanding of the basics and problems of bioinformatics, as well as the applications of data mining technologies in tackling the problems and the essential research topics in the field. Advanced Data Mining Technologies in Bioinformaticsis extremely useful for data mining researchers, molecular biologists, graduate students, and others interested in this topic.

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Saturs

Chapter I Introduction to Data Mining in Bioinformatics
1
Chapter II Hierarchical Profiling Scoring and Applications in Bioinformatics
13
Methods and Practices of Combining Multiple Scoring Systems
32
Chapter IV DNA Sequence Visualization
63
Chapter V Proteomics with Mass Spectrometry
85
Chapter VI Efficient and Robust Analysis of Large Phylogenetic Datasets
104
Chapter VII Algorithmic Aspects of Protein Threading
118
Chapter VIII Pattern Differentiations and Formulations for Heterogeneous Genomic Data through Hybrid Approaches
136
Chapter XI A Haplotype Analysis System for Genes Discovery of Common Diseases
214
Chapter XII A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences Based on Association Rules
231
Theory and Applications
248
Understanding Annotations in Protein Interaction Networks
269
Toward Automatic Annotation of Genes and Proteins
283
Chapter XVI Comparative Genome Annotation Systems
296
About the Authors
314
Index
324

Chapter IX Parameterless Clustering Techniques for Gene Expression Analysis
155
Chapter X Joint Discriminatory Gene Selection for Molecular Classification of Cancer
174

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