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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.
About the Author:
Dr. Hui-Huang Hsu received the B.E. degree from Tamkang University, Taipei, Taiwan in 1987, and the M.S. and Ph.D. degrees from The University of Florida, Florida, USA in 1991 and 1994, respectively. All the degrees are in electrical engineering. He worked as a research assistant in the Department of Computer and Information Sciences at The University of Florida throughout his Ph.D. study and stayed there as a postdoctoral research fellow from January to May in 1995. From August 1995 to July 2003, he was with Chung Kuo Institute of Technology (1995~2000), Takming College (2000~2001), and Chinese Culture University (2001~2003) all located in Taipei, Taiwan. He joined the Department of Computer Science and Information Engineering, TamkangUniversity in August 2003, where he is now an associate professor. He has participated in the organization of several international conferences and workshops as the general chair, the program chair, the workshop chair, the financial chair, and the local arrangement chair. His current research interests are in the areas of bioinformatics, multimedia processing, data mining, machine learning, and e-learning. Dr. Hsu is a senior member of the IEEE.
Collaborative Geographic Information Systems
| Ch. I | Introduction to data mining in bioinformatics | 1 |
| Ch. II | Hierarchical profiling, scoring and applications in bioinformatics | 13 |
| Ch. III | Combinatorial fusion analysis : methods and practices of combining multiple scoring systems | 32 |
| Ch. IV | DNA sequence visualization | 63 |
| Ch. V | Proteomics with mass spectrometry | 85 |
| Ch. VI | Efficient and robust analysis of large phylogenetic datasets | 104 |
| Ch. VII | Algorithmic aspects of protein threading | 118 |
| Ch. VIII | Pattern differentiations and formulations for heterogeneous genomic data through hybrid approaches | 136 |
| Ch. IX | Parameterless clustering techniques for gene expression analysis | 155 |
| Ch. X | Joint discriminatory gene selection for molecular classification of cancer | 174 |
| Ch. XI | A haplotype analysis system for genes discovery of common disease | 214 |
| Ch. XII | A Bayesian framework for improving clustering accuracy of protein sequences based on association rules | 231 |
| Ch. XIII | In silico recognition of protein-protein interactions : theory and applications | 248 |
| Ch. XIV | Differential association rules : understanding annotations in protein interaction networks | 269 |
| Ch. XV | Mining bio literature : toward automatic annotation of genes and proteins | 283 |
| Ch. XVI | Comparative genome annotation systems | 296 |
Overview
Advanced Data Mining Technologies in Bioinformatics covers important research ...