Knowledge Discovery in Bioinformatics
Techniques, Methods, and Applications
(Sprache: Englisch)
The purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and...
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Produktinformationen zu „Knowledge Discovery in Bioinformatics “
The purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and structural motifs, genomics and proteomics, gene findings, drug design, RNAi and microRNA analysis, text mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, system biology and pathways, and biological database management.
Klappentext zu „Knowledge Discovery in Bioinformatics “
Wiley Series on Bioinformatics: Computational Techniques and EngineeringDiscover how data mining is fueling new discoveries in bioinformatics
As the field of bioinformatics continues to flourish, producing enormous amounts of new data, the need for sophisticated methods of data mining to better manage and extract meaning from bioinformatics data has grown tremendously. This pioneering text brings together an unparalleled group of leading experts in both data mining and bioinformatics. These experts present a broad range of novel methods, techniques, and applications of data mining for the analysis and management of bioinformatics data sets. Among the topics covered are:
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RNA and protein structure analysis
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DNA computing
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Sequence mapping and genome comparison
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Gene expression data mining
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Metabolic network modeling
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Phyloinformatics
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Biomedical literature data mining
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Biological data integration and searching
For each topic, readers get an inside perspective into the latest research-what works and what doesn't and where additional research and development is needed. References to the primary literature facilitate further in-depth research.
Data mining in bioinformatics holds the promise of solving such fundamental problems as protein structure, gene finding, data retrieval, and integration. This text is therefore essential reading for all researchers in bioinformatics, pointing them to new methods and techniques that may be the key to new and important discoveries.
Inhaltsverzeichnis zu „Knowledge Discovery in Bioinformatics “
Contributors.Preface.
1 Current Methods for Protein Secondary-Structure Prediction Based on Support Vector Machines (Hae-Jin Hu, Robert W. Harrison, Phang C. Tai, and Yi Pan).
2 Comparison of Seven Methods for Mining Hidden Links (Xiaohua Hu, Xiaodan Zhang, and Xiaohua Zhou).
3 Voting Scheme-Based Evolutionary Kernel Machines for Drug Activity Comparisons (Bo Jin and Yan-Qing Zhang).
4 Bioinformatics Analyses of Arabidopsis thaliana Tiling Array Expression Data (Trupti Joshi, Jinrong Wan, Curtis J. Palm, Kara Juneau, Ron Davis, Audrey Southwick, Katrina M. Ramonell, Gary Stacey, and Dong Xu).
5 Identification of Marker Genes from High-Dimensional Microarray Data for Cancer Classification (Jiexun Li, Hua Su, and Hsinchun Chen).
6 Patient Survival Prediction from Gene Expression Data (Huiqing Liu, Limsoon Wong, and Ying Xu).
7 RNA Interference and microRNA (Shibin Qiu and Terran Lane).
8 Protein Structure Prediction Using String Kernels (Huzefa Rangwala, Kevin DeRonne, and George Karypis).
9 Public Genomic Databases: Data Representation, Storage, and Access (Andrew Robinson, Wenny Rahayu, and David Taniar).
10 Automatic Query Expansion with Keyphrases and POS Phrase Categorization for Effective Biomedical Text Mining (Min Song and Il-Yeol Song).
11 Evolutionary Dynamics of Protein-Protein Interactions (L. S. Swapna, B. Offmann, and N. Srinivasan).
12 On Comparing and Visualizing RNA Secondary Structures (Jason T. L. Wang, Dongrong Wen, and Jianghui Liu).
13 Integrative Analysis of Yeast Protein Translation Networks (Daniel D. Wu and Xiaohua Hu).
14 Identification of Transmembrane Proteins Using Variants of the Self-Organizing Feature Map Algorithm (Mary Qu Yang, Jack Y. Yang, and Craig W. Codrington).
15 TRICLUSTER: Mining Coherent Clusters in Three-Dimensional Microarray Data (Lizhuang Zhao and Mohammed J. Zaki).
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Clustering Methods in a Protein-Protein Interaction Network (Chuan Lin, Young-Rae Cho, Woo-Chang Hwang, Pengjun Pei, and Aidong Zhang).
References.
Index.
References.
Index.
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Autoren-Porträt
Xiaohua Hu, PhD, is Assistant Professor of Computer Science in the College of Information Science and Technology at Drexel University. His research has been published in such journals as IEEE Computer, Knowledge and Information Systems, Journal of Intelligent Systems, and the International Journal of Applied Intelligence.Yi Pan, PhD, is Chair and Professor of Computer Science at Georgia State University. His pioneering work in computing using reconfigurable optical buses has been cited by researchers around the world. Dr. Pan is co-holder of three United States patents (pending) and five provisional patents.
Bibliographische Angaben
- 2007, 1. Auflage, 400 Seiten, Maße: 24,1 cm, Gebunden, Englisch
- Herausgegeben: Xiaohua Hu, Yi Pan
- Verlag: Wiley & Sons
- ISBN-10: 047177796X
- ISBN-13: 9780471777960
Sprache:
Englisch
Pressezitat
"...this book is essential reading for all researchers in bioinformatics." ( Bioautomation , volume 7)
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