Advanced Data Mining Technologies in Bioinformatics, 1st Edition

  • Published By:
  • ISBN-10: 1591408652
  • ISBN-13: 9781591408659
  • Grade Level Range: College Freshman - College Senior
  • 329 Pages | eBook
  • Original Copyright 2006 | Published/Released November 2006
  • This publication's content originally published in print form: 2006

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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.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Advanced Data Mining Technologies in Bioinformatics: Table of Contents.
1: Introduction to Data Mining in Bioinformatics.
2: Hierarchical Profiling, Scoring and Applications in Bioinformatics.
3: Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems.
4: DNA Sequence Visualization.
5: Proteomics with Mass Spectrometry.
6: Efficient and Robust Analysis of Large Phylogenetic Datasets.
7: Algorithmic Aspects of Protein Threading.
8: Pattern Differentiations and Formulations for Heterogeneous Genomic Data through Hybrid Approaches.
9: Parameterless Clustering Techniques for Gene Expression Analysis.
10: Joint Discriminatory Gene Selection for Molecular Classification of Cancer.
11: A Haplotype Analysis System for Genes Discovery of Common Diseases.
12: A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences Based on Association Rules.
13: In Silico Recognition of Protein-Protein Interactions: Theory and Applications.
14: Differential Association Rules: Understanding Annotations in Protein Interaction Networks.
15: Mining BioLiterature: Toward Automatic Annotation of Genes and Proteins.
16: Comparative Genome Annotation Systems.
About the Authors.