Computational intelligence in bioinformatics
著者
書誌事項
Computational intelligence in bioinformatics
(IEEE series on computational intelligence / David B. Fogel, series editor)
IEEE Press , Wiley-Interscience, c2008
大学図書館所蔵 全7件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
Combining biology, computer science, mathematics, and statistics, the field of bioinformatics has become a hot new discipline with profound impacts on all aspects of biology and industrial application. Now, Computational Intelligence in Bioinformatics offers an introduction to the topic, covering the most relevant and popular CI methods, while also encouraging the implementation of these methods to readers' research.
目次
Preface. Contributors.
Part One Gene Expression Analysis and Systems Biology.
1. Hybrid of Neural Classifi er and Swarm Intelligence in Multiclass Cancer Diagnosis with Gene Expression Signatures (Rui Xu, Georgios C. Anagnostopoulos, and Donald C. Wunsch II).
1.1 Introduction.
1.2 Methods and Systems.
1.3 Experimental Results.
1.4 Conclusions.
2. Classifying Gene Expression Profi les with Evolutionary Computation (Jin-Hyuk Hong and Sung-Bae Cho).
2.1 DNA Microarray Data Classifi cation.
2.2 Evolutionary Approach to the Problem.
2.3 Gene Selection with Speciated Genetic Algorithm.
2.4 Cancer Classifi ction Based on Ensemble Genetic Programming.
2.5 Conclusion.
3. Finding Clusters in Gene Expression Data Using EvoCluster (Patrick C. H. Ma, Keith C. C. Chan, and Xin Yao).
3.1 Introduction.
3.2 Related Work.
3.3 Evolutionary Clustering Algorithm.
3.4 Experimental Results.
3.5 Conclusions.
4. Gene Networks and Evolutionary Computation (Jennifer Hallinan).
4.1 Introduction.
4.2 Evolutionary Optimization.
4.3 Computational Network Modeling.
4.4 Extending Reach of Gene Networks.
4.5 Network Topology Analysis.
4.6 Summary.
Part Two Sequence Analysis and Feature Detection.
5. Fuzzy-Granular Methods for Identifying Marker Genes from Microarray Expression Data (Yuanchen He, Yuchun Tang, Yan-Qing Zhang, and Rajshekhar Sunderraman).
5.1 Introduction.
5.2 Traditional Algorithms for Gene Selection.
5.3 New Fuzzy-Granular-Based Algorithm for Gene Selection.
5.4 Simulation.
5.5 Conclusions.
6. Evolutionary Feature Selection for Bioinformatics (Laetitia Jourdan, Clarisse Dhaenens, and El-Ghazali Talbi).
6.1 Introduction.
6.2 Evolutionary Algorithms for Feature Selection.
6.3 Feature Selection for Clustering in Bioinformatics.
6.4 Feature Selection for Classifi cation in Bioinformatics.
6.5 Frameworks and Data Sets.
6.6 Conclusion.
7. Fuzzy Approaches for the Analysis CpG Island Methylation Patterns (Ozy Sjahputera, Mihail Popescu, James M. Keller, and Charles W. Caldwell).
7.1 Introduction.
7.2 Methods.
7.3 Biological Signifi cance.
7.4 Conclusions.
Part Three Molecular Structure and Phylogenetics.
8. Protein-Ligand Docking with Evolutionary Algorithms(Rene Thomsen).
8.1 Introduction.
8.2 Biochemical Background.
8.3 The Docking Problem.
8.4 Protein-Ligand Docking Algorithms.
8.5 Evolutionary Algorithms.
8.6 Effect of Variation Operators.
8.7 Differential Evolution.
8.8 Evaluating Docking Methods.
8.9 Comparison between Docking Methods.
8.10 Summary.
8.11 Future Research Topics.
9. RNA Secondary Structure Prediction Employing Evolutionary Algorithms (Kay C. Wiese, Alain A. Deschenes, and Andrew G. Hendriks).
9.1 Introduction.
9.2 Thermodynamic Models.
9.3 Methods.
9.4 Results.
9.5 Conclusion.
10. Machine Learning Approach for Prediction of Human Mitochondrial Proteins (Zhong Huang, Xuheng Xu, and Xiaohua Hu).
10.1 Introduction.
10.2 Methods and Systems.
10.3 Results and Discussion.
10.4 Conclusions.
11. Phylogenetic Inference Using Evolutionary Algorithms(Clare Bates Congdon).
11.1 Introduction.
11.2 Background in Phylogenetics.
11.3 Challenges and Opportunities for Evolutionary Computation.
11.4 One Contribution of Evolutionary Computation: Graphyl.
11.5 Some Other Contributions of Evolutionary computation.
11.6 Open Questions and Opportunities.
Part Four Medicine.
12. Evolutionary Algorithms for Cancer Chemotherapy Optimization (John McCall, Andrei Petrovski, and Siddhartha Shakya).
12.1 Introduction.
12.2 Nature of Cancer.
12.3 Nature of Chemotherapy.
12.4 Models of Tumor Growth and Response.
12.5 Constraints on Chemotherapy.
12.6 Optimal Control Formulations of Cancer Chemotherapy.
12.7 Evolutionary Algorithms for Cancer Chemotherapy Optimization.
12.8 Encoding and Evaluation.
12.9 Applications of EAs to Chemotherapy Optimization Problems.
12.10 Related Work.
12.11 Oncology Workbench.
12.12 Conclusion.
13. Fuzzy Ontology-Based Text Mining System for Knowledge Acquisition, Ontology Enhancement, and Query Answering from Biomedical Texts (Lipika Dey and Muhammad Abulaish).
13.1 Introduction.
13.2 Brief Introduction to Ontologies.
13.3 Information Retrieval form Biological Text Documents: Related Work.
13.4 Ontology-Based IE and Knowledge Enhancement System.
13.5 Document Processor.
13.6 Biological Relation Extractor.
13.7 Relation-Based Query Answering.
13.8 Evaluation of the Biological Relation Extraction Process.
13.9 Biological Relation Characterizer.
13.10 Determining Strengths of Generic Biological Relations.
13.11 Enhancing GENIA to Fuzzy Relational Ontology.
13.12 Conclusions and Future Work.
References.
Appendix Feasible Biological Relations.
Index.
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