LEADER 04286nam 22006492 450 001 9910464949703321 005 20151005020621.0 010 $a1-139-89115-4 010 $a1-107-27191-6 010 $a1-107-27524-5 010 $a1-107-27849-X 010 $a1-299-74933-X 010 $a1-107-27726-4 010 $a1-139-22570-7 010 $a1-107-27400-1 035 $a(CKB)3460000000128952 035 $a(EBL)1303669 035 $a(OCoLC)852158546 035 $a(SSID)ssj0000917848 035 $a(PQKBManifestationID)11956889 035 $a(PQKBTitleCode)TC0000917848 035 $a(PQKBWorkID)10893468 035 $a(PQKB)10883935 035 $a(UkCbUP)CR9781139225700 035 $a(MiAaPQ)EBC1303669 035 $a(Au-PeEL)EBL1303669 035 $a(CaPaEBR)ebr10729876 035 $a(CaONFJC)MIL506184 035 $a(EXLCZ)993460000000128952 100 $a20111216d2013|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aComplexity and the arrow of time /$fedited by Charles H. Lineweaver, Australian National University, Paul C.W. Davies, Arizona State University, Michael Ruse, Florida State University$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2013. 215 $a1 online resource (xii, 357 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a1-107-02725-X 311 $a1-107-27252-1 320 $aIncludes bibliographical references and index. 327 $aWhat 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 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. 330 $aThere 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. 517 3 $aComplexity & the Arrow of Time 606 $aComplexity (Philosophy) 606 $aScience$xPhilosophy 615 0$aComplexity (Philosophy) 615 0$aScience$xPhilosophy. 676 $a003 702 $aLineweaver$b C. H$g(Charley H.), 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910464949703321 996 $aComplexity and the arrow of time$92465024 997 $aUNINA LEADER 05551nam 2200541 a 450 001 9910146082503321 005 20170809171351.0 010 $a1-280-55646-3 010 $a9786610556465 010 $a0-471-46118-0 010 $a0-471-22758-7 035 $a(CKB)1000000000018954 035 $a(EBL)705365 035 $a(OCoLC)815646727 035 $a(SSID)ssj0000290043 035 $a(PQKBManifestationID)11220388 035 $a(PQKBTitleCode)TC0000290043 035 $a(PQKBWorkID)10402854 035 $a(PQKB)10914445 035 $a(MiAaPQ)EBC705365 035 $a(EXLCZ)991000000000018954 100 $a20020522d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 12$aA biologist's guide to analysis of DNA microarray data$b[electronic resource] /$fSteen Knudsen 210 $aNew York $cWiley-Interscience$dc2002 215 $a1 online resource (148 p.) 300 $aDescription based upon print version of record. 311 $a0-471-22490-1 320 $aIncludes bibliographical references (p. 104-122) and index. 327 $aMachine 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 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. 330 $aA 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. 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