Emerging trends in applications and infrastructures for computational biology, bioinformatics, and systems biology : systems and applications

• Main Authors: Tran, Quoc-Nam, Arabnia, Hamid
• Format: eBook Book
• Language: English
• Published: Cambridge, Mass Morgan Kaufmann, an imprint of Elsevier 2016; Elsevier Science & Technology; Morgan Kaufmann
• Edition: 1
• Series: Emerging trends in computer science and applied computing
• Subjects: Bioinformatics; Computational biology; Computational biology. fast (OCoLC)fst00871990; Medical Informatics
• ISBN: 9780128042038; 0128042036
• DOI: 10.1016/C2015-0-01779-8

Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: Systems and Applications covers the latest trends in the field with special emphasis on their applications. The first part covers the major areas of computational biology, development...

Table of Contents:

• Appendix D.3. Watson-Crick Base Pairing Constraint -- Appendix D.4. Base Pair Stacking Constraint -- References -- Chapter 10: Effects of Excessive Water Intake on Body-Fluid Homeostasis and the Cardiovascular System - a Computer Simulat ... -- 10.1. Introduction -- 10.2. Computational Model -- 10.2.1. Cardiovascular hemodynamics: CVSim -- 10.2.2. Body-Fluid Homeostasis: A Renal Function Model -- 10.2.3. Coupling of Systems Models -- 10.3. Results and Validation -- 10.3.1. Modeling of Short-Term Responses -- 10.3.1.1. Cardiovascular responses -- 10.3.1.2. Body-fluid homeostasis -- 10.3.2. Modeling of Long-Term Responses to Chronic Excessive Water Intake -- 10.4. Conclusions -- References -- Chapter 11: A DNA-Based Migration Modeling of the Lizards in Florida Scrub Habitat -- 11.1. Introduction -- 11.2. Related Works -- 11.3. Methodology -- 11.3.1. ECON Clustering -- 11.3.2. Discovery of Migration Patterns -- 11.3.3. Analysis of the Migration Patterns -- 11.4. Empirical Results -- 11.5. Conclusion and Future Research -- References -- Chapter 12: Reconstruction of Gene Regulatory Networks Using Principal Component Analysis -- 12.1. Introduction -- 12.2. Methods -- 12.2.1. State Space Model -- 12.2.2. Simplified Linear Model -- 12.3. Results and Discussion -- 12.4. Conclusion -- References -- Chapter 13: nD-PDPA: n-Dimensional Probability Density Profile Analysis -- 13.1. Introduction -- 13.2. Residual Dipolar Coupling -- 13.3. Method -- 13.4. Scoring of nD-PDPA -- 13.5. Data Preparation -- 13.6. Results and Discussion -- 13.6.1. Experiment 1 -- 13.6.2. Experiment 2 -- 13.6.3. Experiment 3 -- 13.6.4. Experiment 4 -- 13.7. Conclusion -- References -- Chapter 14: Biomembranes Under Oxidative Stress: Insights from Molecular Dynamics Simulations -- 14.1. Introduction -- 14.2. Theoretical Modeling -- 14.3. Case Studies
• Chapter 4: Spontaneous Activity Characterization in Spiking Neural Systems With Log-Normal Synaptic Weight Distribution -- 4.1. Introduction -- 4.2. Models of Spontaneous Activity -- 4.3. Model and Methods -- 4.3.1. LIF Neural System Applied Synaptic Input -- 4.3.2. Izhikevich Neural System Used for Synaptic Input -- 4.3.3. Evaluation Indices -- 4.4. Results and Evaluations -- 4.4.1. Effect of Input Spike From Weak Synapse in LIF Neural System -- 4.4.2. Spike Transmission in LIF Neural System -- 4.4.3. Spike Transmission in Izhikevich Neural System -- 4.5. Conclusions -- References -- Chapter 5: Comparison Between OpenMP and Mpich Optimized Parallel Implementations of a Cellular Automaton that Simulates th ... -- 5.1. Introduction -- 5.1.1. The Cellular Automaton Game of Life -- 5.2. MPICH Optimized Approach of the Cellular Automaton -- 5.2.1. MPI Standard -- 5.2.2. Description of the MPICH Approach of the Cellular Automaton -- 5.2.3. MPICH Implementation of the Cellular Automaton -- Code 1. Program code of the MPICH version of Game of Life -- 5.3. OpenMP Optimized Approach of the Cellular Automaton -- 5.3.1. Open Multiprocessing -- 5.3.2. Description of the OpenMP Approach of the Cellular Automaton -- 5.3.3. OpenMP Implementation of the Cellular Automaton -- Code 2. Program code of the OpenMP version of Game of Life -- 5.4. Execution Time Comparison of the Two Parallel Implementations -- 5.5. Conclusions -- References -- Section II: Bioinformatics, Simulation, Data Mining, Pattern Discovery, and Prediction Methods -- Chapter 6: Structure Calculation of α, α/β, β Proteins from Residual Dipolar Coupling Data Using Redcraft -- 6.1. Introduction -- 6.2. Background and Method -- 6.2.1. Residual Dipolar Couplings -- 6.2.2. REDCRAFT Structural Fitness Calculation -- 6.2.3. The Ensemble of Test Proteins -- 6.2.4. Simulated RDC Data -- 6.2.5. Evaluation
• 14.3.1. Permeability of Biomembranes to ROS -- 14.3.2. Photosensitizer Interaction With Membranes -- 14.4. Outlook -- 14.5. Conclusion and Summary -- References -- Chapter 15: Feature Selection and Classification Of Microarray Data Using Machine Learning Techniques -- 15.1. Introduction -- 15.2. Literature Review -- 15.3. Methodology Used -- 15.3.1. Feature Selection Methodology -- 15.3.1.1. t-Statistic -- 15.3.1.2. F-test/ANOVA/(BSS/WSS) -- 15.3.1.3. Wilcoxon rank sum -- 15.3.1.4. χ2-test -- 15.3.1.5. Signal-to-noise ratio -- 15.3.1.6. Information gain -- 15.3.1.7. Fisher score -- 15.3.1.8. Gini index -- 15.3.2. Classification Methodologies -- 15.3.2.1. Logistic regression classifier -- 15.3.2.2. Naive Bayes classifier -- 15.3.2.3. Artificial neural network -- 15.3.2.4. Radial basis function network -- 15.3.2.5. Probabilistic neural network -- 15.3.2.6. K-nearest neighbor -- 15.3.2.7. Support vector machine -- 15.4. Performance Evaluation Parameters -- 15.5. Empirical Analysis of Existing Techniques -- 15.5.1. Results and Interpretation -- 15.5.1.1. Different data sets used -- 15.5.1.2. Logistic regression -- 15.5.1.3. Naive Bayes -- 15.5.1.4. Artificial neural network -- 15.5.1.5. Radial basis function network -- 15.5.1.6. Probabilistic neural network -- 15.5.1.7. K-nearest neighbor -- 15.5.1.8. Support vector machine -- 15.5.1.9. Comparative analysis -- 15.6. Conclusion -- References -- Chapter 16: New Directions in Deterministic Metabolism Modeling of Sheep -- 16.1. Introduction -- 16.2. Advantages of Whole-Body Metabolism Modeling -- 16.3. Review of Work to Date -- 16.4. Outcomes -- 16.5. Summary -- 16.6. Future Work -- References -- Chapter 17: Differentiating Cancer from Normal Protein-Protein Interactions Through Network Analysis -- 17.1. Introduction -- 17.2. Related Literature -- 17.3. Network Analysis: Proposed Methods -- 17.3.1. Data Set
• Front Cover -- Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: System ... -- Copyright -- Contents -- List of Contributors -- Preface -- Introduction -- Acknowledgments -- Section I: Computational Biology - Methodologies and Algorithms -- Chapter 1: Using Methylation Patterns for Reconstructing Cell Division Dynamics: Assessing Validation Experiments -- 1.1. Introduction -- 1.1.1. Using Methylation Patterns -- 1.1.2. Bisulfite Treatment -- 1.2. Errors, Biases, and Uncertainty in Bisulfite Sequencing -- 1.3. Model for Degradation and Sampling -- 1.3.1. Modeling -- 1.3.2. Simulation Study: Effects of Degradation -- 1.4. Statistical Inference Method -- 1.5. Simulation Study: Bayesian Inference -- 1.6. Discussion -- 1.6.1. Different Experiments -- 1.6.2. Opportunities -- 1.6.3. Conclusions -- References -- Chapter 2: A Directional Cellular Dynamic Under the Control of a Diffusing Energy for Tissue Morphogenesis: Phenotype and ... -- 2.1. Introduction -- 2.2. Mathematical Morphological Dynamics -- 2.2.1. Gene and Status Expression -- 2.3. Attainable Sets of Phenotypes -- 2.3.1. Implementation -- 2.4. Prediction Tool Based on a Coevolution of a Dynamic Tissue with an Energy Diffusion -- 2.4.1. Prediction of Tissue Growth -- 2.4.2. Energy Diffusion Model -- 2.4.2.1. Mitosis -- 2.4.2.2. Quiescence -- 2.4.2.3. Apoptosis -- 2.4.3. Results -- 2.5. Discussion -- References -- Chapter 3: A Feature Learning Framework for Histology Images Classification -- 3.1. Introduction -- 3.2. Methods -- 3.2.1. Color and Color Spaces -- 3.2.2. Features Extraction and Classification -- 3.3. Proposed System -- 3.4. Image Data Sets -- 3.5. Experimental Results -- 3.6. Conclusion -- References
• 6.3. Results and Discussion -- 6.3.1. Structure Calculation of an α Protein -- 6.3.2. Structure Calculation of an α/β Protein -- 6.3.3. Structure Calculation of a β Protein -- 6.3.4. Effect of Error in Structure Calculation -- 6.4. Conclusion -- References -- Chapter 7: Architectural Topography of the α-Subunit Cytoplasmic Loop in the Gabaa Receptor -- 7.1. Introduction -- 7.2. Methodological Approach -- 7.3. Results and Discussion -- 7.3.1. Sequence Comparison of α Subunits -- 7.3.2. Subdomains of the ILD -- 7.3.3. Sequence Patterns in the ILD -- 7.3.4. Protein Architecture of the ILD -- 7.3.5. Role of the α1 ILD in GABAAR -- 7.4. Conclusions -- References -- Chapter 8: Finding Long-Term Influence and Sensitivity of Genes Using Probabilistic Genetic Regulatory Networks -- 8.1. Introduction -- 8.2. Influence and Sensitivity Factors of Genes in PBNs -- 8.2.1. Influence Factor of Genes -- 8.2.2. Impact Factor of Genes -- 8.2.3. Boolean Algebra -- 8.3. A Biological Case Study -- 8.3.1. Gliomas Case Study -- 8.3.2. Stable Genes -- 8.3.3. Sensitive Genes -- 8.3.4. Hi-Impact Genes -- 8.4. Conclusion -- References -- Chapter 9: The Application of Grammar Space Entropy in Rna Secondary Structure Modeling -- 9.1. Introduction -- 9.2. A Shannon Entropy for the SCFG Space -- 9.2.1. An Intuitive Example -- 9.2.2. Generalization to All Structurally Unambiguous Grammars -- 9.3. GS Entropy of RNA Folding Models -- 9.4. The Typical Set Criterion -- 9.4.1. Future Work in TSC-Based Model Training Algorithms -- 9.5. Discussion and Conclusions -- Appendix A. Calculating Sum of Probabilities of Derivations in an SCFG -- Appendix B. Computing GS Entropy of an SCFG -- Appendix C. An Example of Calculating the GS Entropy -- Appendix D. GS Entropy of the Basic Grammar -- Appendix D.1. Grammar Description -- Appendix D.2. Calculations
• 17.3.2. Basic Network Properties