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[null,"1.","Record Nr.",null,"UNINA9910464949703321"]
[null,null,"Titolo",null,"Complexity and the arrow of time / / edited by Charles H. Lineweaver, Australian National University, Paul C.W. Davies, Arizona State University, Michael Ruse, Florida State University [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"Cambridge : , : Cambridge University Press, , 2013"]
[null,null,"ISBN",null,"1-139-89115-4"]
[{"$nulls":4},"1-107-27191-6"]
[{"$nulls":4},"1-107-27524-5"]
[{"$nulls":4},"1-107-27849-X"]
[{"$nulls":4},"1-299-74933-X"]
[{"$nulls":4},"1-107-27726-4"]
[{"$nulls":4},"1-139-22570-7"]
[{"$nulls":4},"1-107-27400-1"]
[null,null,"Descrizione fisica",null,"1 online resource (xii, 357 pages) : digital, PDF file(s)"]
[null,null,"Disciplina",null,"003"]
[null,null,"Soggetti",null,"Complexity (Philosophy)"]
[{"$nulls":4},"Science - Philosophy"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from publisher's bibliographic system (viewed on 05 Oct 2015)."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"What is complexity? Is it increasing? / Charles H. Lineweaver, Paul C.W. Davies, and Michael Ruse -- Directionality principles from cancer to cosmology / Paul C.W. Davies -- A simple treatment of complexity : cosmological entropic boundary conditions on increasing complexity / Charles H. Lineweaver -- Using complexity science to search for unity in the natural sciences / Eric J. Chaisson -- On the spontaneous generation of complexity in the universe / Seth Lloyd -- Emergent spatiotemporal complexity in field theory / Marcelo Gleiser -- Life : the final frontier for complexity? / Simon Conway Morris -- Evolution beyond Newton, Darwin, and entailing law : the origin of complexity in the evolving biosphere / Stuart A. Kauffman -- Emergent order in processes : the interplay of complexity, robustness, correlation, and hierarchy in the biosphere / D. Eric Smith -- The inferential evolution of biological complexity : forgetting nature by learning to nurture / David C. Krakauer -- Information width : a way for the second law to increase complexity / David H. Wolpert -- Wrestling with biological "]
[{"$nulls":4},"complexity : from Darwin to Dawkins / Michael Ruse -- The role of generative entrenchment and robustness in the evolution of complexity / William C. Wimsatt -- On the plurality of complexity-producing mechanisms / Philip Clayton."]
[null,null,null,"Sommario/riassunto","There is a widespread assumption that the universe in general, and life in particular, is 'getting more complex with time'. This book brings together a wide range of experts in science, philosophy and theology and unveils their joint effort in exploring this idea. They confront essential problems behind the theory of complexity and the role of life within it: what is complexity? When does it increase, and why? Is the universe evolving towards states of ever greater complexity and diversity? If so, what is the source of this universal enrichment? This book addresses those difficult questions, and offers a unique cross-disciplinary perspective on some of the most profound issues at the heart of science and philosophy. Readers will gain insights in complexity that reach deep into key areas of physics, biology, complexity science, philosophy and religion."]
[null,"2.","Record Nr.",null,"UNINA9910146082503321"]
[null,null,"Autore",null,"Knudsen Steen"]
[null,null,"Titolo",null,"A biologist's guide to analysis of DNA microarray data [[electronic resource] /] / Steen Knudsen"]
[null,null,"Pubbl/distr/stampa",null,"New York, : Wiley-Interscience, c2002"]
[null,null,"ISBN",null,"1-280-55646-3"]
[{"$nulls":4},"9786610556465"]
[{"$nulls":4},"0-471-46118-0"]
[{"$nulls":4},"0-471-22758-7"]
[null,null,"Descrizione fisica",null,"1 online resource (148 p.)"]
[null,null,"Disciplina",null,"572.8/636"]
[{"$nulls":4},"572.8636"]
[null,null,"Soggetti",null,"DNA microarrays"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 104-122) and index."]
[null,null,"Nota di contenuto",null,"Machine generated contents note: Preface xi -- Acknowledgments xiii -- 1 Introduction I -- 1.1 Hybridization 1 -- 1.2 Affymetrix GeneChip Technology 3 -- 1.3 Spotted Arrays 6 -- 1.4 Serial Analysis of Gene Expression (SAGE) 8 -- 1.5 Example: Affymetrix vs. Spotted Arrays 9 -- 1.6 Summary 11 -- 1.7 Further Reading 13 -- 2 Overview of Data Analysis 15 -- 3 Basic Data Analysis 17 -- 3.1 Absolute Measurements 17 -- 3.2 Scaling 18 -- 3.2.1 Example: Linear and Nonlinear Scaling 20 -- 3.3 Detection of Outliers 20 -- 3.4 Fold Change 21 -- 3.5 Significance 22 -- 3.5.1 Nonparametric Tests 24 -- 3.5.2 Correction for Multiple Testing 24 -- 3.5.3 Example I: t-Test and ANOVA 25 -- 3.5.4 Example II: Number of Replicates 26 -- 3.6 Summary 28 -- 3.7 Further Reading 29 -- 4 Visualization by Reduction of Dimensionality 33 -- 4.1 Principal Component Analysis 33 -- 4.2 Example 1: PCA on Small Data Matrix 35 -- 4.3 Example 2: PCA on Real Data 37 -- 4.4 Summary 37 -- 4.5 Further Reading 39 -- 5 Cluster Analysis 41 -- 5.1 Hierarchical Clustering 41 -- 5.2 K-means Clustering 43 -- 5.3 Self-Organizing Maps 44 -- 5.4 Distance Measures 45 -- 5.4.1 Example: Comparison of Distance Measures 47 -- 5.5 Normalization 49 -- 5.6 Visualization of Clusters 50 -- 5.6.1 Example: Visualization of Gene Clusters in -- Bladder Cancer 50 -- 5.7 Summary 50 -- 5.8 Further Reading 52 -- 6 Beyond Cluster Analysis 55 -- 6.1 Function Prediction "]
[{"$nulls":4},"55 -- 6.2 Discovery of Regulatory Elements in Promoter -- Regions 56 -- 6.2.1 Example 1: Discovery of Proteasomal Element 57 -- 6.2.2 Example 2: Rediscovery of Mlu Cell Cycle -- Box (MCB) 57 -- 6.3 Integration of data 58 -- 6.4 Summary 59 -- 6.5 Further Reading 59 -- 7 Reverse Engineering of Regulatory Networks 63 -- 7.1 The Time-Series Approach 63 -- 7.2 The Steady-State Approach 64 -- 7.3 Limitations of Network Modeling 65 -- 7.4 Example 1: Steady-State Model 65 -- 7.5 Example 2: Steady-State Model on Real Data 66 -- 7.6 Example 3: Steady-State Model on Real Data 68 -- 7.7 Example 4: Linear Time-Series Model 68 -- 7.8 Further Reading 71 -- 8 Molecular Classifiers 75 -- 8.1 Classification Schemes 76 -- 8.1.1 Nearest Neighbor 76 -- 8.1.2 Neural Networks 76 -- 8.1.3 Support Vector Machine 76 -- 8.2 Example I: Classification of Cancer Subtypes 77 -- 8.3 Example II: Classification of Cancer Subtypes 78 -- 8.4 Summary 79 -- 8.5 Further Reading 79 -- 9 Selection of Genes for Spotting on Arrays 81 -- 9.1 Gene Finding 82 -- 9.2 Selection of Regions Within Genes 82 -- 9.3 Selection of Primers for PCR 83 -- 9.4 Selection of Unique Oligomer Probes 83 -- 9.4.1 Example: Finding PCR Primers for Gene -- AF105374 83 -- 9.5 Experimental Design 84 -- 9.6 Further Reading 84 -- 10 Limitations of Expression Analysis 87 -- 10.1 Relative VersusAbsoluteRNA Quantification 88 -- 10.2 Further Reading 88 -- 11 Genotyping Chips 91 -- 11.1 Example: NeuralNetworksfor GeneChipprediction 91 -- 11.2 Further Reading 93 -- 12 Software Issues and Data Formats 95 -- 12.1 Standardization Efforts 96 -- 12.2 Standard File Format 97 -- 12.2.1 Example: Small Scripts in Awk 97 -- 12.3 Software for Clustering 98 -- 12.3.1 Example: Clustering with ClustArray 99 -- 12.4 Software for Statistical Analysis 99 -- 12.4.1 Example: StatisticalAnalysis with R 99 -- 12.4.2 The affyR Software Package 103 -- 12.4.3 Commercial Statistics Packages 103 -- 12.5 Summary 103 -- 12.6 Further Reading 104 -- 13 Commercial Software Packages 105 -- 14 Bibliography 109 -- Index 123."]
[null,null,null,"Sommario/riassunto","A great introductory book that details reliable approaches to problems met instandard microarray data analyses. It provides examples of establishedapproaches such as cluster analysis, function prediction, and principle component analysis. Discover real examples to illustrate the key concepts of data analysis. Written for those without any advanced background in math, statistics, or computer sciences, this book is essential for anyone interested in harnessing the immense potential of microarrays in biology and medicine."]