Advanced Data Mining Technologies in BioinformaticsHsu, Hui-Huang Idea Group Inc (IGI), 2006. gada 31. marts - 342 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 Bioinformatics is extremely useful for data mining researchers, molecular biologists, graduate students, and others interested in this topic. |
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... methods and more potential applications are also discussed. Chapter III presents a method, called Combinatorial Fusion Analysis (CFA), for analyzing combination and fusion of multiple scoring systems. Both rank combination and score ...
... methods are developed based on resulting profiles from several clustering methods. The developed hybrid analysis is demonstrated through an application to a time course gene expression data from interferon-β-1a treated multiple ...
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Saturs
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 |
314 | |
324 | |
Chapter IX Parameterless Clustering Techniques for Gene Expression Analysis | 155 |
Chapter X Joint Discriminatory Gene Selection for Molecular Classification of Cancer | 174 |