生物信息学知识新发现

作者简介

The purpose of this edited book is tobring togetherthe ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edgeresearch topicssuch 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.

书籍目录

ContributorsPreface1 Current Methods for Protein Secondary-Structure Prediction Based on Support Vector Machines (Hae-Jin Hu, Robert WHarrison, Phang CTai, and Yi Pan)　1.2 Support Vector Machine Method　1.3 Performance Comparison of SVM Methods　1.4 Discussion and Conclusions2 Comparison of Seven Methods for Mining Hidden Links (Xiaohua Hu, Xiaodan Zhang, and Xiaohua Zhou)　2.1 Analysis of the Literature on Raynaud’s Disease　2.2 Related Work　2.3 Methods　2.4 Experiment Results and Analysis　2.5 Discussion and Conclusions3 Voting Scheme–Based Evolutionary Kernel Machines for Drug Activity Comparisons (Bo Jin and Yan-Qing Zhang)　3.1 Granular Kernel and Kernel Tree Design　3.2 GKTSESs　3.3 Evolutionary Voting Kernel Machines　3.4 Simulations　3.5 Conclusions and Future Work4 Bioinformatics Analyses of Arabidopsis thaliana Tiling Array Expression Data (Trupti Joshi, Jinrong Wan, Curtis JPalm, Kara Juneau, Ron Davis, Audrey Southwick, Katrina MRamonell, Gary Stacey, and Dong Xu)　4.1 Tiling Array Design and Data Description　4.2 Ontology Analyses　4.3 Antisense Regulation Identification　4.4 Correlated Expression Between Two DNA Strands.　4.5 Identification of Nonprotein Coding mRNA　4.6 Summary5 Identification of Marker Genes from High-Dimensional Microarray Data for Cancer Classification  (Jiexun Li, Hua Su, and Hsinchun Chen)　5.1 Feature Selection　5.2 Gene Selection　5.3 Comparative Study of Gene Selection Methods　5.4 Conclusions and Discussion6 Patient Survival Prediction from Gene Expression Data (Huiqing Liu, Limsoon Wong, and Ying Xu)　6.1 General Methods　6.2 Applications　6.3 Incorporating Data Mining Techniques to Survival Prediction　6.4 Selection of Extreme Patient Samples　6.5 Summary and Concluding Remarks7 RNA Interference and microRNA (Shibin Qiu and Terran Lane)　7.1 Mechanisms and Applications of RNA Interference　7.2 Specificity of RNA Interference　7.3 Computational Methods for microRNAs　7.4 siRNA Silencing Efficacy　7.5 Summary and Open Questions.8 Protein Structure Prediction Using String Kernels (Huzefa Rangwala, Kevin DeRonne, and George Karypis)　8.1 Protein Structure: Granularities　8.2 Learning from Data　8.3 Structure Prediction: Capturing the Right Signals　8.4 Secondary-Structure Prediction　8.5 Remote Homology and Fold Prediction　8.6 Concluding Remarks9 Public Genomic Databases: Data Representation, Storage, and Access  (Andrew Robinson, Wenny Rahayu, and David Taniar)　9.1 Data Representation　9.2 Data Storage　9.3 Data Access　9.4 Discussion　9.5 Conclusions10 Automatic Query Expansion with Keyphrases and POS Phrase Categorization for Effective Biomedical Text Mining  (Min Song and Il-Yeol Song)　10.1 Keyphrase Extraction-Based Pseudo-Relevance Feedback　10.2 Query Expansion with WordNet　10.3 Experiments on Medline Data Sets　10.4 Conclusions11 Evolutionary Dynamics of Protein–Protein Interactions  (LSSwapna, BOffmann, and NSrinivasan)　11.1 Class I Glutamine Amidotransferase–Like Superfamily　11.2 Drifts in Interfaces of Close Homologs　11.3 Drifts in Interfaces of Divergent Members　11.4 Drifts in Interfaces at Extreme Divergence　11.5 Conclusions12 On Comparing and Visualizing RNA Secondary Structures (Jason TLWang, Dongrong Wen, and Jianghui Liu)　12.1 Background　12.2 RSmatch　12.3 RSview　12.4 Conclusions13 Integrative Analysis of Yeast Protein Translation Networks (Daniel DWu and Xiaohua Hu)　13.1 Protein Biosynthesis and Translation　13.2 Methods　13.3 Results　13.4 Conclusions14 Identification of Transmembrane Proteins Using Variants of the Self-Organizing Feature Map Algorithm (Mary Qu Yang, Jack YYang, and Craig WCodrington)　14.1 Physiochemical Analysis of Proteins　14.2 Variants of the SOM Algorithm　14.3 Results　14.4 Discussion and Conclusions15 TRICLUSTER: Mining Coherent Clusters in Three-Dimensional Microarray Data (Lizhuang Zhao and Mohammed JZaki)　15.1 Preliminary Concepts　15.2 Related Work　15.3 The TRICLUSTER Algorithm　15.4 Experiments　15.5 Conclusions16 Clustering Methods in a Protein–Protein Interaction Network (Chuan Lin, Young-Rae Cho, Woo-Chang Hwang, Pengjun Pei, and Aidong Zhang)　16.1 Protein–Protein Interaction　16.2 Properties of PPI Networks　16.3 Clustering Approaches　16.4 Validation　16.5 ConclusionsReferencesIndex

内容概要

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.

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