Pattern Recognition in Computational Molecular Biology : Techniques and Approaches.

Intro -- Wiley Series -- Title Page -- Copyright -- Table of Contents -- List of Contributors -- Preface -- Part 1: Pattern Recognition in Sequences -- Chapter 1: Combinatorial Haplotyping Problems -- 1.1 Introduction -- 1.2 Single Individual Haplotyping -- 1.3 Population Haplotyping -- References -- Chapter 2: Algorithmic Perspectives of the String Barcoding Problems -- 2.1 Introduction -- 2.2 Summary of Algorithmic Complexity Results for Barcoding Problems -- 2.3 Entropy-Based Information Content Technique for Designing Approximation Algorithms for String Barcoding Problems -- 2.4 Techniques for Proving Inapproximability Results for String Barcoding Problems -- 2.5 Heuristic Algorithms for String Barcoding Problems -- 2.6 Conclusion -- Acknowledgments -- References -- Chapter 3: Alignment-Free Measures for Whole-Genome Comparison -- 3.1 Introduction -- 3.2 Whole-Genome Sequence Analysis -- 3.3 Underlying Approach -- 3.4 Experimental Results -- 3.5 Conclusion -- Author's Contributions -- 3.6 Acknowledgments -- References -- Chapter 4: A Maximum Likelihood Framework for Multiple Sequence Local Alignment -- 4.1 Introduction -- 4.2 Multiple Sequence Local Alignment -- 4.3 Motif Finding Algorithms -- 4.4 Time Complexity -- 4.5 Case Studies -- 4.6 Conclusion -- References -- Chapter 5: Global Sequence Alignment with a Bounded Number of Gaps -- 5.1 Introduction -- 5.2 Definitions and Notation -- 5.3 Problem Definition -- 5.4 Algorithms -- 5.5 Conclusion -- References -- Part 2: Pattern Recognition in Secondary Structures -- Chapter 6: A Short Review on Protein Secondary Structure Prediction Methods -- 6.1 Introduction -- 6.2 Representative Protein Secondary Structure Prediction Methods -- 6.3 Evaluation of Protein Secondary Structure Prediction Methods -- 6.4 Conclusion -- Acknowledgments -- References.

Chapter 7: A Generic Approach to Biological Sequence Segmentation Problems: Application to Protein Secondary Structure Prediction -- 7.1 Introduction -- 7.2 Biological Sequence Segmentation -- 7.3 MSVMpred -- 7.4 Postprocessing with A Generative Model -- 7.5 Dedication to Protein Secondary Structure Prediction -- 7.6 Conclusions and Ongoing Research -- Acknowledgments -- References -- Chapter 8: Structural Motif Identification and Retrieval: A Geometrical Approach -- 8.1 Introduction -- 8.2 A Few Basic Concepts -- 8.3 State of The Art -- 8.4 A Novel Geometrical Approach to Motif Retrieval -- 8.5 Implementation Notes -- 8.6 Conclusions and Future Work -- Acknowledgment -- References -- Chapter 9: Genome-Wide Search for Pseudoknotted Noncoding RNA: A Comparative Study -- 9.1 Introduction -- 9.2 Background -- 9.3 Methodology -- 9.4 Results and Interpretation -- 9.5 Conclusion -- References -- Part 3: Pattern Recognition in Tertiary Structures -- Chapter 10: Motif Discovery in Protein 3D-Structures using Graph Mining Techniques -- 10.1 Introduction -- 10.2 From Protein 3D-Structures to Protein Graphs -- 10.3 Graph Mining -- 10.4 Subgraph Mining -- 10.5 Frequent Subgraph Discovery -- 10.6 Feature Selection -- 10.7 Feature Selection for Subgraphs -- 10.8 Discussion -- 10.9 Conclusion -- Acknowledgments -- References -- Chapter 11: Fuzzy and Uncertain Learning Techniques for the Analysis and Prediction Of Protein Tertiary Structures -- 11.1 Introduction -- 11.2 Genetic Algorithms -- 11.3 Supervised Machine Learning Algorithm -- 11.4 Fuzzy Application -- 11.5 Conclusion -- References -- Chapter 12: Protein Inter-Domain Linker Prediction -- 12.1 Introduction -- 12.2 Protein Structure Overview -- 12.3 Technical Challenges and Open Issues -- 12.4 Prediction Assessment -- 12.5 Current Approaches.

12.6 Domain Boundary Prediction Using Enhanced General Regression Network -- 12.7 Inter-Domain Linkers Prediction Using Compositional Index and Simulated Annealing -- 12.8 Conclusion -- References -- Chapter 13: Prediction of Proline Cis-Trans Isomerization -- 13.1 Introduction -- 13.2 Methods -- 13.3 Model Evaluation and Analysis -- 13.4 Conclusion -- References -- Part 4: Pattern Recognition in Quaternary Structures -- Chapter 14: Prediction of Protein Quaternary Structures -- 14.1 Introduction -- 14.2 Protein Structure Prediction -- 14.3 Template-Based Predictions -- 14.4 Critical Assessment of Protein Structure Prediction -- 14.5 Quaternary Structure Prediction -- 14.6 Conclusion -- Acknowledgments -- References -- Chapter 15: Comparison of Protein Quaternary Structures by Graph Approaches -- 15.1 Introduction -- 15.2 Similarity in The Graph Model -- 15.3 Measuring Structural Similarity Via MCES -- 15.4 Protein Comparison Via Graph Spectra -- 15.5 Conclusion -- References -- Chapter 16: Structural Domains in Prediction of Biological Protein-Protein Interactions -- 16.1 Introduction -- 16.2 Structural Domains -- 16.3 The Prediction Framework -- 16.4 Feature Extraction and Prediction Properties -- 16.5 Feature Selection -- 16.6 Classification -- 16.7 Evaluation and Analysis -- 16.8 Results and Discussion -- 16.9 Conclusion -- References -- Part 5: Pattern Recognition in Microarrays -- Chapter 17: Content-Based Retrieval of Microarray Experiments -- 17.1 Introduction -- 17.2 Information Retrieval: Terminology and Background -- 17.3 Content-Based Retrieval -- 17.4 Microarray Data and Databases -- 17.5 Methods for Retrieving Microarray Experiments -- 17.6 Similarity Metrics -- 17.7 Evaluating Retrieval Performance -- 17.8 Software Tools -- 17.9 Conclusion and Future Directions -- Acknowledgment -- References.

Chapter 18: Extraction of Differentially Expressed Genes in Microarray Data -- 18.1 Introduction -- 18.2 From Microarray Image to Signal -- 18.3 Microarray Signal Analysis -- 18.4 Algorithms for De Gene Selection -- 18.5 Gene Ontology Enrichment and Gene Set Enrichment Analysis -- 18.6 Conclusion -- References -- Chapter 19: Clustering and Classification Techniques for Gene Expression Profile Pattern Analysis -- 19.1 Introduction -- 19.2 Transcriptome Analysis -- 19.3 Microarrays -- 19.4 RNA-Seq -- 19.5 Benefits and Drawbacks of RNA-Seq and Microarray Technologies -- 19.6 Gene Expression Profile Analysis -- 19.7 Real Case Studies -- 19.8 Conclusions -- References -- Chapter 20: Mining Informative Patterns in Microarray Data -- 20.1 Introduction -- 20.2 Patterns with Similarity -- 20.3 Conclusion -- References -- Chapter 21: Arrow Plot and Correspondence Analysis Maps for Visualizing The Effects of Background Correction and Normalization Methods on Microarray Data -- 21.1 Overview -- 21.2 Arrow Plot -- 21.3 Significance Analysis of Microarrays -- 21.4 Correspondence Analysis -- 21.5 Impact of The Preprocessing Methods -- 21.6 Conclusions -- Acknowledgments -- References -- Part 6: Pattern Recognition in Phylogenetic Trees -- Chapter 22: Pattern Recognition in Phylogenetics: Trees and Networks -- 22.1 Introduction -- 22.2 Networks and Trees -- 22.3 Patterns and Their Processes -- 22.4 The Types of Patterns -- 22.5 Fingerprints -- 22.6 Constructing Networks -- 22.7 Multi-Labeled Trees -- 22.8 Conclusion -- References -- Chapter 23: Diverse Considerations for Successful Phylogenetic Tree Reconstruction: Impacts from Model Misspecification, Recombination, Homoplasy, and Pattern Recognition -- 23.1 Introduction -- 23.2 Overview on Methods and Frameworks for Phylogenetic Tree Reconstruction.

23.3 Influence of Substitution Model Misspecification on Phylogenetic Tree Reconstruction -- 23.4 Influence of Recombination on Phylogenetic Tree Reconstruction -- 23.5 Influence of Diverse Evolutionary Processes on Species Tree Reconstruction -- 23.6 Influence of Homoplasy on Phylogenetic Tree Reconstruction: The Goals of Pattern Recognition -- 23.7 Concluding Remarks -- Acknowledgments -- References -- Chapter 24: Automated Plausibility Analysis of Large Phylogenies -- 24.1 Introduction -- 24.2 Preliminaries -- 24.3 A NaÏve Approach -- 24.4 Toward a Faster Method -- 24.5 Improved Algorithm -- 24.6 Implementation -- 24.7 Evaluation -- 24.8 Conclusion -- Acknowledgment -- References -- Chapter 25: A New Fast Method for Detecting and Validating Horizontal Gene Transfer Events Using Phylogenetic Trees and Aggregation Functions -- 25.1 Introduction -- 25.2 Methods -- 25.3 Experimental Study -- 25.4 Results and Discussion -- 25.5 Conclusion -- References -- Part 7: Pattern Recognition in Biological Networks -- Chapter 26: Computational Methods for Modeling Biological Interaction Networks -- 26.1 Introduction -- 26.2 Measures/Metrics -- 26.3 Models of Biological Networks -- 26.4 Reconstructing and Partitioning Biological Networks -- 26.5 Ppi Networks -- 26.6 Mining PPI Networks-Interaction Prediction -- 26.7 Conclusions -- References -- Chapter 27: Biological Network Inference at Multiple Scales: From Gene Regulation to Species Interactions -- 27.1 Introduction -- 27.2 Molecular Systems -- 27.3 Ecological Systems -- 27.4 Models and Evaluation -- 27.5 Learning Gene Regulation Networks -- 27.6 Learning Species Interaction Networks -- 27.7 Conclusion -- References -- Chapter 28: Discovering Causal Patterns with Structural Equation Modeling: Application to Toll-Like Receptor Signaling Pathway in Chronic Lymphocytic Leukemia -- 28.1 Introduction.

28.2 Toll-Like Receptors.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.