Computational Intelligence in Bioinformatics. IEEE Press Series on Computational Intelligence

  • ID: 2180691
  • Book
  • 376 Pages
  • John Wiley and Sons Ltd
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Learn how to apply computational intelligence methods to solve problems in bioinformatics

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. However, due to limitations of traditional algorithms, scientists have been increasingly forced to rethink the way they adapt modeling approaches to challenges created by a wealth of biological data. Now, as computational intelligence (CI) has been applied to bioinformatics with promising results, there is a need for a thorough and comprehensive overview of the importance of and recent developments in CI methods across a range of bioinformatics problems. 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.

Organized in self–contained chapters developed and edited by leading educators in their fields, Computational Intelligence in Bioinformatics explains and applies CI methods to problem areas, such as:

  • Gene expression analysis and systems biology

  • Sequence analysis and feature detection

  • Molecular structure and phylogenetics

  • Medicine

Enhanced by an accompanying Web site ([external URL] that includes data sets used in the book, software that can be obtained for open distribution, a selection of the best and easiest–to–use software for the CI techniques covered in the book, and "Challenge Problems" whose progress will be reported and tracked on the site, Computational Intelligence in Bioinformatics serves as a valuable resource for professionals who are interested in applying CI to solve problems in bioinformatics applications, and also accommodates the needs of graduate–level students in bioinformatics courses.

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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(René 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. Deschênes, 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.


Appendix Feasible Biological Relations.


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Gary B. Fogel, PhD, is Vice President of Natural Selection, Inc., and his current research interests focus on the application of computational intelligence methods to problems in the biomedical sciences. He is a senior member of the IEEE and serves as an Associate Editor on three IEEE journals.

David W. Corne holds a Chair in Computer Science at Heriot–Watt University, Edinburgh, Scotland, and his research interests include evolutionary computation, multi–objective optimization, bioinformatics, telecommunications, and general aspects and applications of nature–inspired computation.

Yi Pan, PhD, is Chair and Professor of Computer Science at Georgia State University and his research interests include high–performance computing, networking, and bioinformatics. Dr. Pan has coedited over twenty books and his recent research has been supported by the NSF, NIH, NSFC, AFOSR, AFRL, JSPS, IISF, and the states of Georgia and Ohio.

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