Guide to analysis of DNA microarray data [[electronic resource] /] / Steen Knudsen

• Record Nr.: UNINA9910830332603321
• Autore: Knudsen Steen
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley-Liss, c2004
• ISBN: 1-280-25320-7; 9786610253203; 0-471-67026-X; 0-471-67027-8
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (194 p.)
• Altri autori (Persone): KnudsenSteen
• Disciplina: 572.86; 572.8636
• Soggetti: DNA microarrays
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Originally published under title: A biologist's guide to analysis of DNA microarray data. c2002.
• Nota di bibliografia: Includes bibliographical references (p. 145-164) and index.
• Nota di contenuto: Guide to ANALYSIS OF DNA MICROARRAY DATA; Contents; Preface; Acknowledgments; 1 Introduction to DNA Microarray Technology; 1.1 Hybridization; 1.2 Gold Rush?; 1.3 The Technology Behind DNA Microarrays; 1.3.1 Affymetrix GeneChip Technology; 1.3.2 Spotted Arrays; 1.3.3 Digital Micromirror Arrays; 1.3.4 Inkjet Arrays; 1.3.5 Bead Arrays; 1.3.6 Serial Analysis of Gene Expression (SAGE); 1.4 Parallel Sequencing on Microbead Arrays; 1.4.1 Emerging Technologies; 1.5 Example: Affymetrix vs. Spotted Arrays; 1.6 Summary; 1.7 Further Reading; 2 Overview of Data Analysis; 3 Image Analysis; 3.1 Gridding; 3.2 Segmentation3.3 Intensity Extraction; 3.4 Background Correction; 3.5 Software; 3.5.1 Free Software for Array Image Analysis; 3.5.2 Commercial Software for Array Image Analysis; 3.6 Summary; 3.7 Further Reading; 4 Basic Data Analysis; 4.1 Normalization; 4.1.1 One or More Genes Assumed Expressed at Constant Rate; 4.1.2 Sum of Genes is Assumed Constant; 4.1.3 Subset of Genes is Assumed Constant; 4.1.4 Majority of Genes Assumed Constant; 4.1.5 Spike Controls; 4.2 Dye Bias, Spatial Bias, Print Tip Bias; 4.3 Expression Indices; 4.3.1 Average Difference; 4.3.2 Signal; 4.3.3 Model-Based Expression Index4.3.4 Robust Multiarray Average; 4.3.5 Position Dependent Nearest Neighbor Model; 4.4 Detection of Outliers; 4.5 Fold Change; 4.6 Significance; 4.6.1 Multiple Conditions; 4.6.2 Nonparametric Tests; 4.6.3 Correction for Multiple Testing; 4.6.4 Example I: t-Test and ANOVA; 4.6.5 Example II: Number of Replicates; 4.7 Mixed Cell Populations; 4.8 Summary; 4.9 Further Reading; 5 Visualization by Reduction of Dimensionality; 5.1 Principal Component Analysis; 5.2 Example 1: PCA on Small Data Matrix; 5.3 Example 2: PCA on Real Data; 5.4 Summary; 5.5 Further Reading; 6 Cluster Analysis6.1 Hierarchical Clustering; 6.2 K-means Clustering; 6.3 Self-organizing Maps; 6.4 Distance Measures; 6.4.1 Example: Comparison of Distance Measures; 6.5 Time-Series Analysis; 6.6 Gene Normalization; 6.7 Visualization of Clusters; 6.7.1 Example: Visualization of Gene Clusters in Bladder Cancer; 6.8 Summary; 6.9 Further Reading; 7 Beyond Cluster Analysis; 7.1 Function Prediction; 7.2 Discovery of Regulatory Elements in Promoter Regions; 7.2.1 Example 1: Discovery of Proteasomal Element; 7.2.2 Example 2: Rediscovery of Mlu Cell Cycle Box (MCB); 7.3 Summary; 7.4 Further Reading; 8 Automated Analysis, Integrated Analysis, and Systems Biology8.1 Integrated Analysis; 8.2 Systems Biology; 8.3 Further Reading; 9 Reverse Engineering of Regulatory Networks; 9.1 The Time-Series Approach; 9.2 The Steady-State Approach; 9.3 Limitations of Network Modeling; 9.4 Example 1: Steady-State Model; 9.5 Example 2: Steady-State Model on Bacillus Data; 9.6 Example 3: Linear Time-Series Model; 9.7 Further Reading; 10 Molecular Classifiers; 10.1 Feature Selection; 10.2 Validation; 10.3 Classification Schemes; 10.3.1 Nearest Neighbor; 10.3.2 Nearest Centroid; 10.3.3 Neural Networks; 10.3.4 Support Vector Machine
• Sommario/riassunto: Written for biologists and medical researchers who don't have any special training in data analysis and statistics, Guide to Analysis of DNA Microarray Data, Second Edition begins where DNA array equipment leaves off: the image produced by the microarray. The text deals with the questions that arise starting at this point, providing an introduction to microarray technology, then moving on to image analysis, data analysis, cluster analysis, and beyond.With all chapters rewritten, updated, and expanded to include the latest generation of technology and methods, Guide to Analysis of DNA Micro

Sixième méditation cartésienne / Eugen Fink

• Record Nr.: UNINA9910862399403321
• Autore: Fink, Eugen
• Pubbl/distr/stampa: [Grenoble], : J. Millon
• Descrizione fisica: volumi ; 22 cm
• Locazione: FLFBC
• Collocazione: DAM A91.14 DESR/S 13 (1)
• Lingua di pubblicazione: Francese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 1. : L'idée d'une théorie transcendantale de la méthode; texte établi et édité par Hans Ebeling, Jann Holl et Guy Van Kerckhoven ; traduit de l'allemand par Nathalie Depraz

Supervised learning with complex-valued neural networks / / Sundaram Suresh, Narasimhan Sundararajan, and Ramasamy Savitha

• Record Nr.: UNINA9910437919903321
• Autore: Suresh Sundaram
• Pubbl/distr/stampa: Heidelberg ; ; New York, : Springer, c2013
• ISBN: 9783642294914; 364229491X
• Edizione: [1st ed. 2013.]
• Descrizione fisica: 1 online resource (XXII, 170 p.)
• Collana: Studies in computational intelligence, , 1860-949X ; ; 421
• Altri autori (Persone): SundararajanNarasimhan; SavithaRamasamy
• Disciplina: 006.31
• Soggetti: Supervised learning (Machine learning); Neural networks (Computer science)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Introduction -- Fully Complex-valued Multi Layer Perceptron Networks -- Fully Complex-valued Radial Basis Function Networks -- Performance Study on Complex-valued Function Approximation Problems -- Circular Complex-valued Extreme Learning Machine Classifier -- Performance Study on Real-valued Classification Problems -- Complex-valued Self-regulatory Resource Allocation Network -- Conclusions and Scope for FutureWorks (CSRAN).
• Sommario/riassunto: Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.