Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology

Systems and Applications
 
 
Morgan Kaufmann (Verlag)
  • 1. Auflage
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  • erschienen am 25. März 2016
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  • 592 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-0-12-804259-5 (ISBN)
 

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


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)
  • Englisch
  • San Diego
  • |
  • USA
Elsevier Science
  • 26,66 MB
978-0-12-804259-5 (9780128042595)
0128042591 (0128042591)
weitere Ausgaben werden ermittelt
  • 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
  • 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 a, a/ß, ß 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
  • 6.3. Results and Discussion
  • 6.3.1. Structure Calculation of an a Protein
  • 6.3.2. Structure Calculation of an a/ß Protein
  • 6.3.3. Structure Calculation of a ß Protein
  • 6.3.4. Effect of Error in Structure Calculation
  • 6.4. Conclusion
  • References
  • Chapter 7: Architectural Topography of the a-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 a 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 a1 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
  • 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
  • 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. ?2-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
  • 17.3.2. Basic Network Properties
  • 17.4. Analysis and Results
  • 17.4.1. Hub Node Analysis
  • 17.4.2. Centrality Analysis
  • 17.4.3. Common Subgraph Analysis
  • 17.4.3.1. Degree analysis
  • 17.4.3.2. Transitivity analysis
  • 17.4.4. Bipartite Graph Analysis
  • 17.5. Discussion and Conclusions
  • References
  • Chapter 18: Predicting the Co-Receptors of the Viruses that Cause Aids (HIV-1) in Cd4 Cells
  • 18.1. Introduction
  • 18.2. Antecedents
  • 18.2.1. Retrovirus of the Class VI
  • 18.2.2. Classification of Retrovirus
  • 18.2.3. Vital Cycle of Retrovirus
  • 18.3. Retrovirus More Common in Humans
  • 18.3.1. Cellular Damages Causes by HIV
  • 18.3.2. Detection of the HIV in Humans
  • 18.4. The Tropism of AIDS
  • 18.5. Materials and Methods
  • 18.5.1. Data Collection
  • 18.5.2. Generation of Characteristics
  • 18.5.3. Results
  • 18.6. Conclusions
  • References
  • Section III: Systems Biology and Biological Processes
  • Chapter 19: Cellular Automata-Based Modeling of Three-Dimensional Multicellular Tissue Growth
  • 19.1. Introduction
  • 19.2. Related Work
  • 19.3. Modeling of Biological Processes
  • 19.3.1. Cell Division
  • 19.3.2. Cell Motion
  • 19.3.3. Cell Collision
  • 19.3.4. Cell Aggregation
  • 19.4. Computational Model
  • 19.5. Algorithm
  • 19.5.1. Initial Condition and Inputs
  • 19.5.2. Iterative Operations
  • 19.5.3. Division Routine
  • 19.5.4. Direction Change Routine
  • 19.6. Calculations of Tissue Growth Rate
  • 19.7. Simulation Results and Discussion
  • 19.7.1. Effect of Cell Heterogeneity on Volume Coverage and Tissue Growth Rate
  • 19.7.2. Comparison of the Two Uniform Cell Seeding Distributions
  • 19.8. Conclusion and Future Work
  • Definitions of Key Terms
  • References
  • Chapter 20: A Combination of Protein-Protein Interaction Network Topological and Biological Process Features for Multiprot ...
  • 20.1. Introduction
  • 20.2. Method
  • 20.2.1. Features Extraction
  • 20.2.2. Preparing and Preprocessing of the Data
  • 20.2.3. Mining Patterns (Classification)
  • 20.2.4. Postprocessing Patterns
  • 20.3. Experimental Work and Results
  • 20.4. Conclusion
  • References
  • Chapter 21: Infogenomics: Genomes as Information Sources
  • 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
  • References
  • 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
  • 22.1. Introduction
  • 22.2. Methods
  • 22.2.1. Data Set
  • 22.2.2. Binning
  • 22.2.3. StickWRLD Analysis
  • 22.3. Results
  • 22.3.1. Binning Variation 1
  • 22.3.2. Binning Variation 2
  • 22.3.3. Comparison of Correlations Between Binning Variations
  • 22.4. Discussion
  • 22.5. Conclusions
  • References
  • Chapter 23: Structural and Percolation Models of Intelligence: to the Question of the Reduction of the Neural Network
  • 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
  • References
  • Section V: Medical Applications and Systems
  • Chapter 24: Analyzing TCGA Lung Cancer Genomic and Expression Data Using SVM with Embedded Parameter Tuning
  • 24.1. Introduction
  • 24.2. Methods
  • 24.3. Results and Discussion
  • 24.4. Conclusions
  • Supplementary Materials
  • Competing interests
  • Authors' contributions
  • References
  • Chapter 25: State-of-the-Art Mock Human Blood Circulation Loop: Prototyping and Introduction of a New Heart Simulator
  • 25.1. Introduction
  • 25.1.1. Background
  • 25.1.1.1. MCLs with biological pump systems
  • 25.1.1.2. MCLs with VAD pumps
  • 25.1.1.3. MCLs with piston pumps
  • 25.1.1.4. MCLs with pressurized chambers
  • 25.2. Novel Design of MCL
  • 25.2.1. LV Heart Simulator Design of MCL
  • 25.3. Conclusions
  • References
  • Chapter 26: Framework for an Interactive Assistance in Diagnostic Processes Based on Probabilistic Modeling of Clinical Pr ...
  • 26.1. Introduction
  • 26.2. Approach of Modeling CPGs
  • 26.3. Construction of the Interface
  • 26.3.1. State of the Art
  • 26.3.2. Modeling of a Particular Disease
  • 26.4. Bayesian Nets
  • 26.4.1. State of the Art
  • 26.4.2. Transformation of a UML Activity
  • 26.4.3. Network Structure
  • 26.4.4. Network Parameters
  • 26.4.5. Assistance Function
  • 26.5. Verification and Validation
  • 26.6. Conclusion
  • References
  • Chapter 27: Motion Artifacts Compensation in DCE-MRI Framework Using Active Contour Model
  • 27.1. Introduction
  • 27.2. DCE Technique
  • 27.3. Active Contour
  • 27.4. Methodology and Implementation
  • 27.4.1. Select ROI
  • 27.4.2. Identification of the ROI
  • 27.4.2.1. Image filtering
  • 27.4.2.2. Clustering
  • 27.4.2.3. Canny edge
  • 27.4.2.4. Chamfer distance transform
  • 27.4.3. Active Contour
  • 27.4.4. Extending the Boundaries of the Entire Temporal Sequence
  • 27.5. Tracking Motion
  • 27.6. Results
  • 27.7. Conclusions
  • References
  • Chapter 28: Phase III Placebo-Controlled, Randomized Clinical Trial with Synthetic Crohn's Disease Patients to Evaluate Tre ...
  • 28.1. Introduction
  • 28.2. Materials and Methods
  • 28.2.1. Study Design
  • 28.2.2. Data Extraction
  • 28.2.3. Data Generation
  • 28.2.3.1. Estimating missing values
  • 28.2.3.2. Creation of a synthetic CD patient population
  • 28.2.3.3. Inclusion and exclusion criteria
  • 28.2.3.4. In silico therapeutic interventions
  • Placebo
  • Conjugated linoleic acid
  • GED-0301
  • LANCL2 therapeutics
  • 28.2.4. Data Analytics
  • 28.3. Results
  • 28.4. Discussion
  • References
  • Chapter 29: Pathological Tissue Permittivity Distribution Difference Imaging: Near-Field Microwave Tomographic Image for B ...
  • 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
  • 29.5.1. Quarter of Iteration of FRFT Algorithm
  • 29.5.2. The Influences to Sinusoidal Signal Structure by Quarter Iteration of FRFT
  • 29.5.3. Processing Results Comparison Between Sf0(n) and Sf1(n)
  • 29.6. Microwave Image of Sagittal Iterative Reconstruction Algorithm
  • 29.6.1. Mathematical Description
  • 29.6.2. Reconstruction Method Based on Sagittal Distribution Characteristics
  • 29.6.3. Algorithm Convergence Statement
  • 29.7. BRATUMASS Clinical Trials
  • 29.7.1. Screening and Locating of the Breast Cancer's Clinical Trials Case
  • 29.7.2. Interventional Therapeutic Pattern Before and After Surgical Operation's Comparison
  • 29.8. Conclusions
  • References
  • Chapter 30: A System for the Analysis of Eeg Data and Brain State Modeling
  • 30.1. Introduction
  • 30.2. System for EEG Data Collection, Storage, and Visualization
  • 30.2.1. EEG Data Collection and Storage
  • 30.2.1.1. EEG headset
  • 30.2.1.2. Data storage
  • 30.2.1.3. Web visualization of EEG data
  • 30.2.1.4. Data modeling interface
  • 30.3. Data Analysis
  • 30.3.1. Data Analysis on Raw Data Sets
  • 30.3.2. Data Analysis on Normalized Mean
  • 30.4. Conclusion
  • 30.5. Future Work
  • References
  • Chapter 31: Using Temporal Logic to Verify the Blood Supply Chain Safety
  • 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
  • 31.5.1. Syntax
  • 31.5.2. Semantics
  • 31.5.3. Mapping Safety Requirements as Assertions in States and State Transitions
  • 31.6. Implementation
  • 31.7. Related Work
  • 31.8. Conclusions
  • References
  • Chapter 32: Evaluation of Window Parameters of CT Brain Images with Statistical Central Moments
  • 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
  • References
  • Chapter 33: An Improved Balloon Snake Algorithm for Ultrasonic Image Segmentation
  • 33.1. Introduction
  • 33.2. Methods
  • 33.2.1. Balloon Snake Model
  • 33.2.2. The MAC Model
  • 33.2.3. Generalized AMBBS Model
  • 33.2.4. The Analysis of the Recovery of Broken Boundary
  • 33.3. Simulation Studies
  • 33.3.1. Capturing Complex Geometries
  • 33.3.2. Resilience to Arbitrary Initializations
  • 33.3.3. Convergence on Weak Edges
  • 33.3.4. Recovering Noise Images
  • 33.4. Experimental Results
  • 33.5. Conclusion
  • References
  • Chapter 34: Brain Ventricle Detection Using Hausdorff Distance
  • 34.1. Introduction
  • 34.2. The Hausdorff Distance
  • 34.3. The Proposed Method
  • 34.3.1. Creating the Model
  • 34.3.2. Ventricles Detection with the Template Produced
  • 34.4. Discussion
  • 34.5. Conclusion
  • References
  • Chapter 35: Tumor Growth Emergent Behavior Analysis Based on Cancer Hallmarks and in a Cancer Stem Cell Context
  • 35.1. Introduction
  • 35.2. Methods
  • 35.3. Results
  • 35.3.1. Emergent Behaviors and Related Hallmarks
  • 35.3.2. Effect of Cancer Stem Cells on the Resultant Behavior
  • 35.4. Conclusions
  • References
  • Index
  • Back Cover

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