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...

Descripción completa

Detalles Bibliográficos
Autor principal: Shmulevich, Ilya
Otros Autores: Dougherty, Edward R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Princeton : Princeton University Press, 2014.
Colección:Book collections on Project MUSE.
Temas:
Acceso en línea:Texto completo

MARC

LEADER 00000cam a22000004a 4500
001 musev2_35305
003 MdBmJHUP
005 20230905043720.0
006 m o d
007 cr||||||||nn|n
008 140823s2014 nju o 00 0 eng d
020 |a 9781400865260 
020 |z 9780691117621 
040 |a MdBmJHUP  |c MdBmJHUP 
100 1 |a Shmulevich, Ilya. 
245 1 0 |a Genomic Signal Processing 
264 1 |a Princeton :  |b Princeton University Press,  |c 2014. 
264 3 |a Baltimore, Md. :  |b Project MUSE,   |c 0000 
264 4 |c ©2014. 
300 |a 1 online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 0 |a Princeton Series in Applied Mathematics 
500 |a 6.2 Cluster Operators. 
505 0 |a 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. 
505 0 |a 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. 
505 0 |a 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. 
505 0 |a 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. 
505 0 |a 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. 
520 |a 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. 
546 |a In English. 
588 |a Description based on print version record. 
650 7 |a Genomics  |x Mathematical models.  |2 fast  |0 (OCoLC)fst00940230 
650 7 |a Genetic regulation.  |2 fast  |0 (OCoLC)fst00940086 
650 7 |a Cellular signal transduction.  |2 fast  |0 (OCoLC)fst00850288 
650 7 |a MATHEMATICS  |x Applied.  |2 bisacsh 
650 7 |a SCIENCE  |x Life Sciences  |x Biochemistry.  |2 bisacsh 
650 6 |a Genomique  |x Modeles mathematiques. 
650 6 |a Regulation genetique. 
650 6 |a Transduction du signal cellulaire. 
650 0 |a Genomics  |x Mathematical models. 
650 0 |a Genetic regulation. 
650 0 |a Cellular signal transduction. 
655 7 |a Electronic books.   |2 local 
700 1 |a Dougherty, Edward R. 
710 2 |a Project Muse.  |e distributor 
830 0 |a Book collections on Project MUSE. 
856 4 0 |z Texto completo  |u https://projectmuse.uam.elogim.com/book/35305/ 
945 |a Project MUSE - Custom Collection