Cargando…
Genomic Signal Processing
Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineerin...
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton :
Princeton University Press,
2014.
|
| Colección: | Book collections on Project MUSE.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title; Copyright; Contents; Preface; 1 Biological Foundations; 1.1 Genetics; 1.1.1 Nucleic Acid Structure; 1.1.2 Genes; 1.1.3 RNA; 1.1.4 Transcription; 1.1.5 Proteins; 1.1.6 Translation; 1.1.7 Transcriptional Regulation; 1.2 Genomics; 1.2.1 Microarray Technology; 1.3 Proteomics; Bibliography; 2 Deterministic Models of Gene Networks; 2.1 Graph Models; 2.2 Boolean Networks; 2.2.1 Cell Differentiation and Cellular Functional States; 2.2.2 Network Properties and Dynamics; 2.2.3 Network Inference; 2.3 Generalizations of Boolean Networks; 2.3.1 Asynchrony; 2.3.2 Multivalued Networks.
- 2.4 Differential Equation Models2.4.1 A Differential Equation Model Incorporating Transcription and Translation; 2.4.2 Discretization of the Continuous Differential Equation Model; Bibliography; 3 Stochastic Models of Gene Networks; 3.1 Bayesian Networks; 3.2 Probabilistic Boolean Networks; 3.2.1 Definitions; 3.2.2 Inference; 3.2.3 Dynamics of PBNs; 3.2.4 Steady-State Analysis of Instantaneously Random PBNs ; 3.2.5 Relationships of PBNs to Bayesian Networks; 3.2.6 Growing Subnetworks from Seed Genes; 3.3 Intervention; 3.3.1 Gene Intervention; 3.3.2 Structural Intervention.
- 3.3.3 External ControlBibliography; 4 Classification; 4.1 Bayes Classifier; 4.2 Classification Rules; 4.2.1 Consistent Classifier Design; 4.2.2 Examples of Classification Rules; 4.3 Constrained Classifiers; 4.3.1 Shatter Coefficient; 4.3.2 VC Dimension; 4.4 Linear Classification; 4.4.1 Rosenblatt Perceptron; 4.4.2 Linear and Quadratic Discriminant Analysis; 4.4.3 Linear Discriminants Based on Least-Squares Error; 4.4.4 Support Vector Machines; 4.4.5 Representation of Design Error for Linear Discriminant Analysis; 4.4.6 Distribution of the QDA Sample-Based Discriminant.
- 4.5 Neural Networks Classifiers4.6 Classification Trees; 4.6.1 Classification and Regression Trees; 4.6.2 Strongly Consistent Rules for Data-Dependent Partitioning; 4.7 Error Estimation; 4.7.1 Resubstitution; 4.7.2 Cross-validation; 4.7.3 Bootstrap; 4.7.4 Bolstering; 4.7.5 Error Estimator Performance; 4.7.6 Feature Set Ranking; 4.8 Error Correction; 4.9 Robust Classifiers; 4.9.1 Optimal Robust Classifiers; 4.9.2 Performance Comparison for Robust Classifiers; Bibliography; 5 Regularization; 5.1 Data Regularization; 5.1.1 Regularized Discriminant Analysis; 5.1.2 Noise Injection.
- 5.2 Complexity Regularization5.2.1 Regularization of the Error; 5.2.2 Structural Risk Minimization; 5.2.3 Empirical Complexity ; 5.3 Feature Selection; 5.3.1 Peaking Phenomenon; 5.3.2 Feature Selection Algorithms; 5.3.3 Impact of Error Estimation on Feature Selection; 5.3.4 Redundancy; 5.3.5 Parallel Incremental Feature Selection; 5.3.6 Bayesian Variable Selection; 5.4 Feature Extraction; Bibliography; 6 Clustering; 6.1 Examples of Clustering Algorithms; 6.1.1 Euclidean Distance Clustering; 6.1.2 Self-Organizing Maps; 6.1.3 Hierarchical Clustering; 6.1.4 Model-Based Cluster Operators.