Advanced Data Mining Technologies in BioinformaticsHui-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. |
No grāmatas satura
1.–5. rezultāts no 35.
. lappuse
Hui-Huang Hsu. Data Minin Technologies i Bioinformatics Hui-Huang Hs Advanced Data Mining Technologies in Bioinformatics Hui-HuangHsu Tamkang University, Taipei,. Advanced Front Cover.
Hui-Huang Hsu. Data Minin Technologies i Bioinformatics Hui-Huang Hs Advanced Data Mining Technologies in Bioinformatics Hui-HuangHsu Tamkang University, Taipei,. Advanced Front Cover.
i. lappuse
Hui-Huang Hsu. Advanced Data Mining Technologies in Bioinformatics Hui-HuangHsu Tamkang University, Taipei, Taiwan IDEA GROUP PUBLISHING Development Editor: Senior Managing Editor: Managing Editor: Copy Editor: Typesetter: Hershey • London ...
Hui-Huang Hsu. Advanced Data Mining Technologies in Bioinformatics Hui-HuangHsu Tamkang University, Taipei, Taiwan IDEA GROUP PUBLISHING Development Editor: Senior Managing Editor: Managing Editor: Copy Editor: Typesetter: Hershey • London ...
iii. lappuse
... . Frank Hsu, Fordham University, USA Yun-Sheng Chung, National Tsing Hua University, Taiwan Bruce S. Kristal, Burke Medical Research Institute and weill Medical College of Cornell University, USA ChapterIV DNA Sequence Visualization ...
... . Frank Hsu, Fordham University, USA Yun-Sheng Chung, National Tsing Hua University, Taiwan Bruce S. Kristal, Burke Medical Research Institute and weill Medical College of Cornell University, USA ChapterIV DNA Sequence Visualization ...
iv. lappuse
... University, USA Salvatore Mungal, Duke University Medical Center , USA Richard Haney, Duke University Medical Center , USA Edward F. Patz, Jr., Duke University Medical Center , USA Patrick McConnell, Duke University Medical Center, USA ...
... University, USA Salvatore Mungal, Duke University Medical Center , USA Richard Haney, Duke University Medical Center , USA Edward F. Patz, Jr., Duke University Medical Center , USA Patrick McConnell, Duke University Medical Center, USA ...
v. lappuse
... University of Tennessee, USA YingXu, University of Georgia, USA Al Geist, Oak Ridge National Laboratory, USA Grant Heffelfinger, Sandia National Laboratories, USA Nagiza F. Samatova, Oak Ridge National Laboratory, USA ChapterXIV ...
... University of Tennessee, USA YingXu, University of Georgia, USA Al Geist, Oak Ridge National Laboratory, USA Grant Heffelfinger, Sandia National Laboratories, USA Nagiza F. Samatova, Oak Ridge National Laboratory, USA ChapterXIV ...
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 |
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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Bieži izmantoti vārdi un frāzes
algorithm alignment analysis annotation applications approach association rule Bioinformatics biological chapter classification clustering combination compared comparison complex component computational considered contribution Copying or distributing Copyright correlation corresponding data mining database dataset defined developed disease distance distributing in print DNA sequences domain electronic forms estimate example experiments Figure forms without written function fusion gene gene expression genetic genome given haplotype hierarchical hour hour 8 hour Idea Group Inc identify important improve independent integrated interactions learning machine matrix measure methods microarray molecular multiple networks observations obtained pair pathways patterns performance phylogenetic positive prediction print or electronic problem profiles proposed protein proteomics rank representation represented respectively samples Science score score functions selected separation sequence shows similarity SNPs sources space statistical structure Table techniques threading tree University values vector visualization