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E-raamat: Genomic Signal Processing

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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 mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.

Arvustused

"Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention... The authors' substantial accomplishments in this area will inspire researchers and students alike. The book provides a much-needed stepping stone so that researchers can cross the gap on this front in their efforts. Also assuredly, it will be a delight to read for anyone who is encountering the topic for the first time and is wishing to exploit the current findings and interpretations in systems biology."--Current Engineering Practice "Overall, this book should be useful for individuals with a background in computer science and machine learning who wish to see the applications of mathematics to genomics."--Leon Glass, SIAM Review

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There is a genuine need for this concise, informative, clearly written book. In systems biology, engineers, mathematicians, and computer scientists are collaborating increasingly with biologists and researchers in medicine. This book goes a long way toward narrowing the gap on this front, and it lays a rigorous foundation for a new discipline. -- Olli Yli-Harja, Tampere University of Technology
Preface ix
1 Biological Foundations
1.1 Genetics
1
1.1.1 Nucleic Acid Structure
2
1.1.2 Genes
5
1.1.3 RNA
6
1.1.4 Transcription
6
1.1.5 Proteins
9
1.1.6 Translation
10
1.1.7 Transcriptional Regulation
12
1.2 Genomics
16
1.2.1 Microarray Technology
17
1.3 Proteomics
20
Bibliography
22
2 Deterministic Models of Gene Networks
2.1 Graph Models
23
2.2 Boolean Networks
30
2.2.1 Cell Differentiation and Cellular Functional States
33
2.2.2 Network Properties and Dynamics
35
2.2.3 Network Inference
49
2.3 Generalizations of Boolean Networks
53
2.3.1 Asynchrony
53
2.3.2 Multivalued Networks
56
2.4 Differential Equation Models
59
2.4.1 A Differential Equation Model Incorporating Transcription and Translation
62
2.4.2 Discretization of the Continuous Differential Equation Model
65
Bibliography
70
3 Stochastic Models of Gene Networks
3.1 Bayesian Networks
77
3.2 Probabilistic Boolean Networks
83
3.2.1 Definitions
86
3.2.2 Inference
97
3.2.3 Dynamics of PBNs
99
3.2.4 Steady-State Analysis of Instantaneously Random PBNs
113
3.2.5 Relationships of PBNs to Bayesian Networks
119
3.2.6 Growing Subnetworks from Seed Genes
125
3.3 Intervention
129
3.3.1 Gene Intervention
130
3.3.2 Structural Intervention
140
3.3.3 External Control
145
Bibliography
151
4 Classification
4.1 Bayes Classifier
160
4.2 Classification Rules
162
4.2.1 Consistent Classifier Design
162
4.2.2 Examples of Classification Rules
166
4.3 Constrained Classifiers
168
4.3.1 Shatter Coefficient
171
4.3.2 VC Dimension
173
4.4 Linear Classification
176
4.4.1 Rosenblatt Perceptron
177
4.4.2 Linear and Quadratic Discriminant Analysis
178
4.4.3 Linear Discriminants Based on Least-Squares Error
180
4.4.4 Support Vector Machines
183
4.4.5 Representation of Design Error for Linear Discriminant Analysis
186
4.4.6 Distribution of the QDA Sample-Based Discriminant
187
4.5 Neural Networks Classifiers
189
4.6 Classification Trees
192
4.6.1 Classification and Regression Trees
193
4.6.2 Strongly Consistent Rules ibr Data-Dependent Partitioning
194
4.7 Error Estimation
196
4.7.1 Resubstitution
196
4.7.2 Cross-validation
198
4.7.3 Bootstrap
199
4.7.4 Bolstering
201
4.7.5 Error Estimator Performance
204
4.7.6 Feature Set Ranking
207
4.8 Error Correction
209
4.9 Robust Classifiers
213
4.9.1 Optimal Robust Classifiers
214
4.9.2 Performance Comparison for Robust Classifiers
216
Bibliography
221
5 Regularization
5.1 Data Regularization
225
5.1.1 Regularized Discriminant Analysis
225
5.1.2 Noise Injection
228
5.2 Complexity Regularization
231
5.2.1 Regularization of the Error
231
5.2.2 Structural Risk Minimization
233
5.2.3 Empirical Complexity
236
5.3 Feature Selection
237
5.3.1 Peaking Phenomenon
237
5.3.2 Feature Selection Algorithms
243
5.3.3 Impact of Error Estimation on Feature Selection
244
5.3.4 Redundancy
245
5.3.5 Parallel Incremental Feature Selection
249
5.3.6 Bayesian Variable Selection
251
5.4 Feature Extraction
254
Bibliography
259
6 Clustering
6.1 Examples of Clustering Algorithms
263
6.1.1 Euclidean Distance Clustering
264
6.1.2 Self-Organizing Maps
265
6.1.3 Hierarchical Clustering
266
6.1.4 Model-Based Cluster Operators
268
6.2 Cluster Operators
269
6.2.1 Algorithm Structure
269
6.2.2 Label Operators
271
6.2.3 Bayes Clusterer
273
6.2.4 Distributional Testing of Cluster Operators
274
6.3 Cluster Validation
276
6.3.1 External Validation
276
6.3.2 Internal Validation
277
6.3.3 Instability Index
278
6.3.4 Bayes Factor
280
6.4 Learning Cluster Operators
281
6.4.1 Empirical-Error Cluster Operator
281
6.4.2 Nearest-Neighbor Clustering Rule
283
Bibliography
292
Index 295


Ilya Shmulevich, an associate professor at the Institute for Systems Biology, is the coauthor of "Microarray Quality Control" and the coeditor of "Computational and Statistical Approaches to Genomics". Edward R. Dougherty is professor of electrical and computer engineering and director of the Genomic Signal Processing Laboratory at Texas A&M University, and director of the Computational Biology Division at the Translational Genomics Research Institute. His thirteen previous books include "Random Processes for Image and Signal Processing".
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