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Biological networks /
This volume presents a timely and comprehensive overview of biological networks at all organization levels in the spirit of the complex systems approach. It discusses the transversal issues and fundamental principles as well as the overall structure, dynamics, and modeling of a wide array of biologi...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore ; Hackensack, NJ :
World Scientific,
©2007.
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| Colección: | Complex systems and interdisciplinary science ;
v. 3. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; Challenges; Outline; Acknowledgements; Contributors; Chapter 1 Scale-Free Networks in Biology Eivind Almaas, Alexei Vázquez and Albert-László Barabási; 1. Introduction; 2. Characterizing Network Topology; 2.1. Degree Distribution; 2.2. Clustering Coefficient; 2.3. Subgraphs and Motifs; 3. Network Models; 3.1. Random Network Model; 3.2. Scale-Free Network Model; 3.3. Hierarchical Network Model; 3.4. Bose-Einstein Condensation and Networks; 4. Network Utilization; 4.1. Flux Utilization; 4.2. Gene Interactions; 5. Conclusion; References
- Chapter 2 Modularity in Biological Networks Ricard V. Solé, Sergi Valverde and Carlos Rodriguez-Caso1. Introduction; 2. Topological Overlap; 3. Modular Networks: The Role of Tinkering; 4. Conclusions; Acknowledgments; References; Chapter 3 Inference of Biological Regulatory Networks: Machine Learning Approaches Florence d'Alché-Buc; 1. Introduction; 1.1. Feasibility of Inference; 1.2. Overview of Methods; 2. The Inference of Gene Regulatory Networks as a Machine Learning Problem; 2.1. Gene Regulatory Networks; 2.2. Machine Learning: A Short Definition
- 2.3. A Methodology for the Conception of a Learning Algorithm3. Representation Issues; 3.1. Prerequisites; 3.2. Questions When Accounting for Dynamics; 3.2.1. Encoding the Data; 3.2.2. Identifiability, Learnability and Sample Complexity; 3.2.3. Time-Scale, Sampling Frequency and Irregular Sampling; 3.2.4. Continuous versus Discretized Encoding; 3.3. Deterministic Models of Dynamics; 3.3.1. Temporal Boolean Network Models; 3.3.2. Linear Networks; 3.3.3. Artificial Recurrent Neural Networks; 3.4. Probabilistic Models of Dynamics; 3.4.1. Linear Models and Linear State-Space Models
- 3.4.2. Dynamical Bayesian Networks Using non Parametric Regression for Conditional Probability Distributions (CPD)3.4.3. Models of Biochemical Processes; 3.5. Static Models of Causal Dependencies; 3.5.1. Bayesian Networks; 3.5.2. Probabilistic Relational Models; 3.5.3. Module Networks; 3.5.4. Factor Graph Networks (FGN); 4. Learning and Optimization; 4.1. Exact Learning and Best-Fit Approaches; 4.2. Statistical Learning; 4.2.1. Mean Squared Error and Weight Decay for Neural Networks; 4.2.2. Maximum A Posteriori Approaches for Learning Parameters of Bayesian Networks; 4.2.3. Structure Learning
- 5. Validation5.1. Introduction to Validation; 5.2. Statistical Validation of Network Inference; 5.2.1. Model Selection via Sampling and Re-sampling Methods; 5.2.2. Prediction on Unseen Data; 5.2.3. Performance Evaluation on Known Networks (Simulated or Real); 5.3. Biological Validation; 6. Conclusion and Perspectives; References; Chapter 4 Transcriptional Networks François Képès; 1. Introduction; 2. Interacting Partners; 2.1. Genes and DNA Regulatory Regions; 2.2. Regulatory Proteins or Dedicated Transcription Factors