Knowledge Discovery in Bioinformatics: Techniques, Methods, and Applications

Xiaohua Hu, Yi Pan

  • 出版商: Wiley
  • 出版日期: 2007-07-01
  • 定價: $4,200
  • 售價: 8.5$3,570
  • 語言: 英文
  • 頁數: 416
  • 裝訂: Hardcover
  • ISBN: 047177796X
  • ISBN-13: 9780471777960
  • 相關分類: 生物資訊 Bioinformatics
  • 立即出貨 (庫存=1)

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Description

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.
 

Table of Contents

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).

1.2 Support Vector Machine Method.

1.3 Performance Comparison of SVM Methods.

1.4 Discussion and Conclusions.

2 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 Conclusions.

3 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 Work.

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).

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 Summary.

5 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 Discussion.

6 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 Remarks.

7 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 Remarks.

 

9 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 Conclusions.

10 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 Conclusions.

11 Evolutionary Dynamics of Protein–Protein Interactions  (L. S. Swapna, B. Offmann, and N. Srinivasan).

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 Conclusions.

12 On Comparing and Visualizing RNA Secondary Structures (Jason T. L. Wang, Dongrong Wen, and Jianghui Liu).

12.1 Background.

12.2 RSmatch.

12.3 RSview.

12.4 Conclusions.

13 Integrative Analysis of Yeast Protein Translation Networks (Daniel D. Wu and Xiaohua Hu).

13.1 Protein Biosynthesis and Translation.

13.2 Methods.

13.3 Results.

13.4 Conclusions.

14 Identification of Transmembrane Proteins Using Variants of the Self-Organizing Feature Map Algorithm (Mary Qu Yang, Jack Y. Yang, and Craig W. Codrington).

14.1 Physiochemical Analysis of Proteins.

14.2 Variants of the SOM Algorithm.

14.3 Results.

14.4 Discussion and Conclusions.

15 TRICLUSTER: Mining Coherent Clusters in Three-Dimensional Microarray Data (Lizhuang Zhao and Mohammed J. Zaki).

15.1 Preliminary Concepts.

15.2 Related Work.

15.3 The TRICLUSTER Algorithm.

15.4 Experiments.

15.5 Conclusions.

16 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 Conclusions.

References.

Index.

商品描述(中文翻譯)

描述

這本編輯過的書的目的是通過討論基因表達、蛋白質/RNA結構預測、親緣關係、序列和結構模式、基因組學和蛋白質組學、基因發現、藥物設計、RNAi和microRNA分析、生物信息學中的文本挖掘、生化途徑建模、生物醫學本體論、系統生物學和途徑、以及生物數據庫管理等前沿研究主題,將數據挖掘研究人員和生物信息學家的想法和發現匯集在一起。

目錄

1. 蛋白質二級結構預測的當前方法(Hae-Jin Hu, Robert W. Harrison, Phang C. Tai和Yi Pan)
1.2 支持向量機方法
1.3 支持向量機方法的性能比較
1.4 討論和結論

2. 挖掘隱藏連結的七種方法的比較(Xiaohua Hu, Xiaodan Zhang和Xiaohua Zhou)
2.1 對Raynaud病文獻的分析
2.2 相關工作
2.3 方法
2.4 實驗結果和分析
2.5 討論和結論

3. 基於投票方案的演化核機器用於藥物活性比較(Bo Jin和Yan-Qing Zhang)
3.1 粒度核和核樹設計
3.2 GKTSESs
3.3 演化投票核機器
3.4 模擬
3.5 結論和未來工作

4. Arabidopsis thaliana平鋪陣列表達數據的生物信息學分析(Trupti Joshi, Jinrong Wan, Curtis J. Palm, Kara Juneau, Ron Davis, Audrey Southwick, Katrina M. Ramonell, Gary Stacey和Dong Xu)
4.1 平鋪陣列設計和數據描述
4.2 語義分析
4.3 反義調控識別
4.4 兩條DNA鏈之間的相關表達
4.5 非蛋白編碼mRNA的識別
4.6 總結

5. 從高維微陣列數據中識別標記基因用於癌症分類(Jiexun Li, Hua Su和Hsinchun Chen)
5.1 特徵選擇
5.2 基因選擇
5.3 基因選擇方法的比較研究
5.4 結論和討論

6. 基因表達數據中的患者生存預測(Huiqing Liu, Limsoon Wong和Ying Xu)
6.1 通用方法
6.2 應用
6.3 數據挖掘技術在生存預測中的應用
6.4 極端患者樣本的選擇
6.5 總結和結論

7. RNA干擾和microRNA(Shibin Qiu和Terran Lane)
7.1 RNA干擾的機制和應用
7.2 RNA干擾的特異性
7.3 微RNA的計算方法
7.4 siRNA沉默效力
7.5 總結和未解決問題

8. 使用蛋白質結構預測