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The Resource Genomic Signal Processing

Genomic Signal Processing

Label
Genomic Signal Processing
Title
Genomic Signal Processing
Creator
Contributor
Subject
Language
eng
Summary
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 engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathema
Member of
Cataloging source
EBLCP
Index
no index present
Language note
In English
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Princeton Series in Applied Mathematics
Genomic Signal Processing
Label
Genomic Signal Processing
Link
http://www.jstor.org/stable/10.2307/j.ctt7zv8mk
Publication
Note
6.2 Cluster Operators
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Antecedent source
unknown
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • 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
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Extent
1 online resource (314 pages).
File format
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Form of item
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Isbn
9781400865260
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Media category
computer
Media MARC source
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Media type code
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Specific material designation
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