Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology
Systems and Applications
- 1st Edition - March 22, 2016
- Latest edition
- Authors: Hamid R Arabnia, Quoc Nam Tran
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 4 2 0 3 - 8
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 4 2 5 9 - 5
Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: Systems and Applications covers the latest trends in the field… Read more
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 and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques for the study of biological and behavioral systems.The second part covers bioinformatics, an interdisciplinary field concerned with methods for storing, retrieving, organizing, and analyzing biological data. The book also explores the software tools used to generate useful biological knowledge.
The third part, on systems biology, explores how to obtain, integrate, and analyze complex datasets from multiple experimental sources using interdisciplinary tools and techniques, with the final section focusing on big data and the collection of datasets so large and complex that it becomes difficult to process using conventional database management systems or traditional data processing applications.
- Explores all the latest advances in this fast-developing field from an applied perspective
- Provides the only coherent and comprehensive treatment of the subject available
- Covers the algorithm development, software design, and database applications that have been developed to foster research
Researchers and graduate students in Computer Science, Bioinformatics, Systems Biology, Big Data, and Data Analytics
- 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
- Abstract
- 1.1 Introduction
- 1.2 Errors, Biases, and Uncertainty in Bisulfite Sequencing
- 1.3 Model for Degradation and Sampling
- 1.4 Statistical Inference Method
- 1.5 Simulation Study: Bayesian Inference
- 1.6 Discussion
- Chapter 2: A Directional Cellular Dynamic Under the Control of a Diffusing Energy for Tissue Morphogenesis: Phenotype and Genotype
- Abstract
- 2.1 Introduction
- 2.2 Mathematical Morphological Dynamics
- 2.3 Attainable Sets of Phenotypes
- 2.4 Prediction Tool Based on a Coevolution of a Dynamic Tissue with an Energy Diffusion
- 2.5 Discussion
- Chapter 3: A Feature Learning Framework for Histology Images Classification
- Abstract
- Acknowledgments
- 3.1 Introduction
- 3.2 Methods
- 3.3 Proposed System
- 3.4 Image Data Sets
- 3.5 Experimental Results
- 3.6 Conclusion
- Chapter 4: Spontaneous Activity Characterization in Spiking Neural Systems With Log-Normal Synaptic Weight Distribution
- Abstract
- Acknowledgment
- 4.1 Introduction
- 4.2 Models of Spontaneous Activity
- 4.3 Model and Methods
- 4.4 Results and Evaluations
- 4.5 Conclusions
- Chapter 5: Comparison Between OpenMP and MPICH Optimized Parallel Implementations of a Cellular Automaton That Simulates the Skin Pigmentation Evolution
- Abstract
- 5.1 Introduction
- 5.2 MPICH Optimized Approach of the Cellular Automaton
- Code 1. Program code of the MPICH version of Game of Life
- Code 2. Program code of the OpenMP version of Game of Life
- Chapter 1: Using Methylation Patterns for Reconstructing Cell Division Dynamics: Assessing Validation Experiments
- 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
- Abstract
- Acknowledgments
- 6.1 Introduction
- 6.2 Background and Method
- 6.3 Results and Discussion
- 6.4 Conclusion
- Chapter 7: Architectural Topography of the α-Subunit Cytoplasmic Loop in the GABAA Receptor
- Abstract
- 7.1 Introduction
- 7.2 Methodological Approach
- 7.3 Results and Discussion
- 7.4 Conclusions
- Chapter 8: Finding Long-Term Influence and Sensitivity of Genes Using Probabilistic Genetic Regulatory Networks
- Abstract
- Acknowledgments
- 8.1 Introduction
- 8.2 Influence and Sensitivity Factors of Genes in PBNs
- 8.3 A Biological Case Study
- 8.4 Conclusion
- Chapter 9: The Application of Grammar Space Entropy in RNA Secondary Structure Modeling
- Abstract
- Acknowledgments
- 9.1 Introduction
- 9.2 A Shannon Entropy for the SCFG Space
- 9.3 GS Entropy of RNA Folding Models
- 9.4 The Typical Set Criterion
- 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
- Chapter 10: Effects of Excessive Water Intake on Body-Fluid Homeostasis and the Cardiovascular System — A Computer Simulation
- Abstract
- 10.1 Introduction
- 10.2 Computational Model
- 10.3 Results and Validation
- 10.4 Conclusions
- Chapter 11: A DNA-Based Migration Modeling of the Lizards in Florida Scrub Habitat
- Abstract
- 11.1 Introduction
- 11.2 Related Works
- 11.3 Methodology
- 11.4 Empirical Results
- 11.5 Conclusion and Future Research
- Chapter 12: Reconstruction of Gene Regulatory Networks Using Principal Component Analysis
- Abstract
- 12.1 Introduction
- 12.2 Methods
- 12.3 Results and Discussion
- 12.4 Conclusion
- Chapter 13: nD-PDPA: n-Dimensional Probability Density Profile Analysis
- Abstract
- 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
- 13.7 Conclusion
- Chapter 14: Biomembranes Under Oxidative Stress: Insights From Molecular Dynamics Simulations
- Abstract
- Acknowledgments
- 14.1 Introduction
- 14.2 Theoretical Modeling
- 14.3 Case Studies
- 14.4 Outlook
- 14.5 Conclusion and Summary
- Chapter 15: Feature Selection and Classification of Microarray Data Using Machine Learning Techniques
- Abstract
- 15.1 Introduction
- 15.2 Literature Review
- 15.3 Methodology Used
- 15.4 Performance Evaluation Parameters
- 15.5 Empirical Analysis of Existing Techniques
- 15.6 Conclusion
- Chapter 16: New Directions in Deterministic Metabolism Modeling of Sheep
- Abstract
- 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
- Chapter 17: Differentiating Cancer From Normal Protein-Protein Interactions Through Network Analysis
- Abstract
- Acknowledgments
- 17.1 Introduction
- 17.2 Related Literature
- 17.3 Network Analysis: Proposed Methods
- 17.4 Analysis and Results
- 17.5 Discussion and Conclusions
- Chapter 18: Predicting the Co-Receptors of the Viruses That Cause AIDS (HIV-1) in CD4 Cells
- Abstract
- 18.1 Introduction
- 18.2 Antecedents
- 18.3 Retrovirus More Common in Humans
- 18.4 The Tropism of AIDS
- 18.5 Materials and Methods
- 18.6 Conclusions
- Chapter 6: Structure Calculation of α, α/β, β Proteins From Residual Dipolar Coupling Data Using REDCRAFT
- Section III: Systems Biology and Biological Processes
- Chapter 19: Cellular Automata-Based Modeling of Three-Dimensional Multicellular Tissue Growth
- Abstract
- Acknowledgments
- 19.1 Introduction
- 19.2 Related Work
- 19.3 Modeling of Biological Processes
- 19.4 Computational Model
- 19.5 Algorithm
- 19.6 Calculations of Tissue Growth Rate
- 19.7 Simulation Results and Discussion
- 19.8 Conclusion and Future Work
- Definitions of Key Terms
- Chapter 20: A Combination of Protein-Protein Interaction Network Topological and Biological Process Features for Multiprotein Complex Detection
- Abstract
- Acknowledgment
- 20.1 Introduction
- 20.2 Method
- 20.3 Experimental Work and Results
- 20.4 Conclusion
- Chapter 21: Infogenomics: Genomes as Information Sources
- Abstract
- 21.1 Introduction
- 21.2 Basic Notation
- 21.3 Research Lines in Infogenomics
- 21.4 Recurrence Distance Distributions
- 21.5 An Informational Measure of Genome Complexity
- 21.6 Extraction of Genomic Dictionaries
- 21.7 Conclusions
- Chapter 19: Cellular Automata-Based Modeling of Three-Dimensional Multicellular Tissue Growth
- Section IV: Data Analytics and Numerical Modeling in Computational Biology and Bioinformatics
- Chapter 22: Analysis of Large Data Sets: A Cautionary Tale of the Perils of Binning Data
- Abstract
- 22.1 Introduction
- 22.2 Methods
- 22.3 Results
- 22.4 Discussion
- 22.5 Conclusions
- Chapter 23: Structural and Percolation Models of Intelligence: To the Question of the Reduction of the Neural Network
- Abstract
- 23.1 Introduction
- 23.2 Abilities of the Brain While Processing Information
- 23.3 Formalized Structural Model of Intellectual Activity
- 23.4 The Percolation Model of Intellectual Activity
- Chapter 22: Analysis of Large Data Sets: A Cautionary Tale of the Perils of Binning Data
- Section V: Medical Applications and Systems
- Chapter 24: Analyzing TCGA Lung Cancer Genomic and Expression Data Using SVM With Embedded Parameter Tuning
- Abstract
- Acknowledgment
- 24.1 Introduction
- 24.2 Methods
- 24.3 Results and Discussion
- 24.4 Conclusions
- Supplementary Materials
- Competing interests
- Authors' contributions
- Chapter 25: State-of-the-Art Mock Human Blood Circulation Loop: Prototyping and Introduction of a New Heart Simulator
- Abstract
- 25.1 Introduction
- 25.2 Novel Design of MCL
- 25.3 Conclusions
- Chapter 26: Framework for an Interactive Assistance in Diagnostic Processes Based on Probabilistic Modeling of Clinical Practice Guidelines
- Abstract
- 26.1 Introduction
- 26.2 Approach of Modeling CPGs
- 26.3 Construction of the Interface
- 26.4 Bayesian Nets
- 26.5 Verification and Validation
- 26.6 Conclusion
- Chapter 27: Motion Artifacts Compensation in DCE-MRI Framework Using Active Contour Model
- Abstract
- 27.1 Introduction
- 27.2 DCE Technique
- 27.3 Active Contour
- 27.4 Methodology and Implementation
- 27.5 Tracking Motion
- 27.6 Results
- 27.7 Conclusions
- Chapter 28: Phase III Placebo-Controlled, Randomized Clinical Trial With Synthetic Crohn's Disease Patients to Evaluate Treatment Response
- Abstract
- Acknowledgments
- 28.1 Introduction
- 28.2 Materials and Methods
- 28.3 Results
- 28.4 Discussion
- Chapter 29: Pathological Tissue Permittivity Distribution Difference Imaging: Near-Field Microwave Tomographic Image for Breast Tumor Visualization
- Abstract
- Acknowledgment
- 29.1 Introduction
- 29.2 The Signals of BRATUMASS
- 29.3 Fourier Diffraction Theorem
- 29.4 Tissue Dielectric Properties and Reflection Coefficient
- 29.5 Quarter of Iteration of Fractional Fourier Transformation Algorithm and the Signal Processing
- 29.6 Microwave Image of Sagittal Iterative Reconstruction Algorithm
- 29.7 BRATUMASS Clinical Trials
- 29.8 Conclusions
- Chapter 30: A System for the Analysis of EEG Data and Brain State Modeling
- Abstract
- Acknowledgments
- 30.1 Introduction
- 30.2 System for EEG Data Collection, Storage, and Visualization
- 30.3 Data Analysis
- 30.4 Conclusion
- 30.5 Future Work
- Chapter 31: Using Temporal Logic to Verify the Blood Supply Chain Safety
- Abstract
- Acknowledgments
- 31.1 Introduction
- 31.2 Formally Modeling Blood Bank Workflows
- 31.3 The Blood Safety Workflow
- 31.4 Updating the YAWL2DVE Translator
- 31.5 Verifying Blood Bank Workflows Against Safety Requirements
- 31.6 Implementation
- 31.7 Related Work
- 31.8 Conclusions
- Chapter 32: Evaluation of Window Parameters of CT Brain Images With Statistical Central Moments
- Abstract
- Acknowledgment
- 32.1 Introduction
- 32.2 Window Setting
- 32.3 Mathematical Description of Central Moments
- 32.4 Results and Discussion
- 32.5 Comparisons
- 32.6 Conclusion
- Chapter 33: An Improved Balloon Snake Algorithm for Ultrasonic Image Segmentation
- Abstract
- Acknowledgements
- 33.1 Introduction
- 33.2 Methods
- 33.3 Simulation Studies
- 33.4 Experimental Results
- 33.5 Conclusion
- Chapter 34: Brain Ventricle Detection Using Hausdorff Distance
- Abstract
- 34.1 Introduction
- 34.2 The Hausdorff Distance
- 34.3 The Proposed Method
- 34.4 Discussion
- 34.5 Conclusion
- Chapter 35: Tumor Growth Emergent Behavior Analysis Based on Cancer Hallmarks and in a Cancer Stem Cell Context
- Abstract
- Acknowledgments
- 35.1 Introduction
- 35.2 Methods
- 35.3 Results
- 35.4 Conclusions
- Chapter 24: Analyzing TCGA Lung Cancer Genomic and Expression Data Using SVM With Embedded Parameter Tuning
- Index
- Edition: 1
- Latest edition
- Published: March 22, 2016
- Language: English
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Hamid R Arabnia
Hamid R. Arabnia is currently a Full Professor of Computer Science at University of Georgia where he has been since October 1987. His research interests include Parallel and distributed processing techniques and algorithms, interconnection networks, and applications in Computational Science and Computational Intelligence (in particular, in image processing, medical imaging, bioinformatics, and other computational intensive problems). Dr. Arabnia is Editor-in-Chief of The Journal of is Associate Editor of IEEE Transactions on Information Technology in Biomedicine . He has over 300 publications (journals, proceedings, editorship) in his area of research in addition he has edited two titles Emerging Trends in ICT Security (Elsevier 2013), and Advances in Computational Biology (Springer 2012).
Affiliations and expertise
Professor of Computer Science, University of Georgia, Athens, GA, USAQT
Quoc Nam Tran
Professor Quoc-Nam Tran is currently Chair and Full Professor of Computer Science at University of South Dakota. He previously served as Chair and Full Professor of Computer Science at the University of Texas at Tyler. His previous positions include: Professor of Computer Science at Lamar University; Visiting Professor at Rice University; Scientist at Wolfram Research, Champaign-Urbana ; and Assistant Professor at University of Linz (Linz, Austria). Professor Tran's research interests include: computational methods and algorithmic foundations; theory of Groebner bases; bioinformatics and computational biology. He has published extensively in his areas of expertise. He has co-edited a number of books, including: "Software Tools and Algorithms for Biological Systems" (2011) and "Advances in Computational Biology" (2010) (Springer) Professor Tran has served on a number of editorial boards and has organized and chaired sessions for premier conferences such as the IEEE International Conference on Bioinformatics and Biomedicine Workshop.
Affiliations and expertise
Chair and Professor of Computer Science, University of South Dakota, Vermillion, SD, USARead Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology on ScienceDirect