Multiblock data fusion in statistics and machine learning : applications in the natural and life sciences / / Age K. Smilde, Tormod Næs, Kristian Hovde Liland

• Record Nr.: UNINA9910566700603321
• Autore: Smilde Age K.
• Pubbl/distr/stampa: Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]; ©2022
• ISBN: 1-119-60097-9; 1-119-60098-7
• Descrizione fisica: 1 online resource (418 pages)
• Disciplina: 519.52
• Soggetti: Science - Statistical methods; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Multiblock Data Fusion in Statistics and Machine Learning -- Contents -- Foreword -- Preface -- List of Figures -- List of Tables -- Part I Introductory Concepts and Theory -- chapnumcolor1 Introduction -- 1.1 Scope of the Book -- 1.2 Potential Audience -- 1.3 Types of Data and Analyses -- 1.3.1 Supervised and Unsupervised Analyses -- 1.3.2 High-, Mid- and Low-level Fusion -- 1.3.3 Dimension Reduction -- 1.3.4 Indirect Versus Direct Data -- 1.3.5 Heterogeneous Fusion -- 1.4 Examples -- 1.4.1 Metabolomics -- 1.4.2 Genomics -- 1.4.3 Systems Biology -- 1.4.4 Chemistry -- 1.4.5 Sensory Science -- 1.5 Goals of Analyses -- 1.6 Some History -- 1.7 Fundamental Choices -- 1.8 Common and Distinct Components -- 1.9 Overview and Links -- 1.10 Notation and Terminology -- 1.11 Abbreviations -- chapnumcolor2 Basic Theory and Concepts -- 2.i General Introduction -- 2.1 Component Models -- 2.1.1 General Idea of Component Models -- 2.1.2 Principal Component Analysis -- 2.1.3 Sparse PCA -- 2.1.4 Principal Component Regression -- 2.1.5 Partial Least Squares -- 2.1.6 Sparse PLS -- 2.1.7 Principal Covariates Regression -- 2.1.8 Redundancy Analysis -- 2.1.9 Comparing PLS, PCovR and RDA -- 2.1.10 Generalised Canonical Correlation Analysis -- 2.1.11 Simultaneous Component Analysis -- 2.2 Properties of Data ; -- 2.2.1 Data Theory -- 2.2.2 Scale-types -- 2.3 Estimation Methods -- 2.3.1 Least-squares Estimation -- 2.3.2 Maximum-likelihood Estimation -- 2.3.3 Eigenvalue Decomposition-based Methods -- 2.3.4 Covariance or Correlation-based Estimation Methods -- 2.3.5 Sequential Versus Simultaneous Methods -- 2.3.6 Homogeneous Versus Heterogeneous Fusion -- 2.4 Within- and Between-block Variation -- 2.4.1 Definition and Example -- 2.4.2 MAXBET Solution -- 2.4.3 MAXNEAR Solution -- 2.4.4 PLS2 Solution -- 2.4.5 CCA Solution -- 2.4.6 Comparing the Solutions.; 2.4.7 PLS, RDA and CCA Revisited -- 2.5 Framework for Common and Distinct Components -- 2.6 Preprocessing -- 2.7 Validation -- 2.7.1 Outliers -- 2.7.1.1 Residuals -- 2.7.1.2 Leverage -- 2.7.2 Model Fit -- 2.7.3 Bias-variance Trade-off -- 2.7.4 Test Set Validation -- 2.7.5 Cross-validation -- 2.7.6 Permutation Testing -- 2.7.7 Jackknife and Bootstrap -- 2.7.8 Hyper-parameters and Penalties -- 2.8 Appendix -- chapnumcolor3 Structure of Multiblock Data -- 3.i General Introduction -- 3.1 Taxonomy -- 3.2 Skeleton of a Multiblock Data Set -- 3.2.1 Shared Sample Mode -- 3.2.2 Shared Variable Mode -- 3.2.3 Shared Variable or Sample Mode -- 3.2.4 Shared Variable and Sample Mode -- 3.3 Topology of a Multiblock Data Set -- 3.3.1 Unsupervised Analysis -- 3.3.2 Supervised Analysis -- 3.4 Linking Structures -- 3.4.1 Linking Structure for Unsupervised Analysis -- 3.4.2 Linking Structures for Supervised Analysis -- 3.5 Summary -- chapnumcolor4 Matrix Correlations -- 4.i General Introduction -- 4.1 Definition -- 4.2 Most Used Matrix Correlations -- 4.2.1 Inner Product Correlation -- 4.2.2 GCD coefficient -- 4.2.3 RV-coefficient -- 4.2.4 SMI-coefficient -- 4.3 Generic Framework of Matrix Correlations -- 4.4 Generalised Matrix Correlations -- 4.4.1 Generalised RV-coefficient -- 4.4.2 Generalised Association Coefficient -- 4.5 Partial Matrix Correlations -- 4.6 Conclusions and Recommendations -- 4.7 Open Issues -- Part II Selected Methods for Unsupervised and Supervised Topologies -- chapnumcolor5 Unsupervised Methods -- 5.i General Introduction -- 5.ii Relations to the General Framework -- 5.1 Shared Variable Mode -- 5.1.1 Only Common Variation -- 5.1.1.1 Simultaneous Component Analysis -- 5.1.1.2 Clustering and SCA -- 5.1.1.3 Multigroup Data Analysis -- 5.1.2 Common, Local, and Distinct Variation -- 5.1.2.1 Distinct and Common Components.; 5.1.2.2 Multivariate Curve Resolution -- 5.2 Shared Sample Mode -- 5.2.1 Only Common Variation -- 5.2.1.1 SUM-PCA -- 5.2.1.2 Multiple Factor Analysis and STATIS -- 5.2.1.3 Generalised Canonical Analysis -- 5.2.1.4 Regularised Generalised Canonical Correlation Analysis -- 5.2.1.5 Exponential Family SCA -- 5.2.1.6 Optimal-scaling -- 5.2.2 Common, Local, and Distinct Variation -- 5.2.2.1 Joint and Individual Variation Explained -- 5.2.2.2 Distinct and Common Components -- 5.2.2.3 PCA-GCA -- 5.2.2.4 Advanced Coupled Matrix and Tensor Factorisation -- 5.2.2.5 Penalised-ESCA -- 5.2.2.6 Multivariate Curve Resolution -- 5.3 Generic Framework -- 5.3.1 Framework for Simultaneous Unsupervised Methods -- 5.3.1.1 Description of the Framework -- 5.3.1.2 Framework Applied to Simultaneous Unsupervised Data Analysis Methods -- 5.3.1.3 Framework of Common/Distinct Applied to Simultaneous Unsupervised Multiblock Data Analysis Methods -- 5.4 Conclusions and Recommendations -- 5.5 Open Issues -- chapnumcolor6 ASCA and Extensions -- 6.i General Introduction -- 6.ii Relations to the General Framework -- 6.1 ANOVA-Simultaneous Component Analysis -- 6.1.1 The ASCA Method -- 6.1.2 Validation of ASCA -- 6.1.2.1 Permutation Testing -- 6.1.2.2 Back-projection -- 6.1.2.3 Confidence Ellipsoids -- 6.1.3 The ASCA+ and LiMM-PCA Methods -- 6.2 Multilevel-SCA -- 6.3 Penalised-ASCA -- ; 6.4 Conclusions and Recommendations -- 6.5 Open Issues -- chapnumcolor7 Supervised Methods -- 7.i General Introduction -- 7.ii Relations to the General Framework -- 7.1 Multiblock Regression: General Perspectives -- 7.1.1 Model and Assumptions -- 7.1.2 Different Challenges and Aims -- 7.2 Multiblock PLS Regression -- 7.2.1 Standard Multiblock PLS Regression -- 7.2.2 MB-PLS Used for Classification -- 7.2.3 Sparse Multiblock PLS Regression (sMB-PLS).; 7.3 The Family of SO-PLS Regression Methods (Sequential and Orthogonalised PLS Regression) -- 7.3.1 The SO-PLS Method -- 7.3.2 Order of Blocks -- 7.3.3 Interpretation Tools -- 7.3.4 Restricted PLS Components and their Application in SO-PLS -- 7.3.5 Validation and Component Selection -- 7.3.6 Relations to ANOVA -- 7.3.7 Extensions of SO-PLS to Handle Interactions Between Blocks -- 7.3.8 Further Applications of SO-PLS -- 7.3.9 Relations Between SO-PLS and ASCA -- 7.4 Parallel and Orthogonalised PLS (PO-PLS) Regression -- 7.5 Response Oriented Sequential Alternation -- 7.5.1 The ROSA Method -- 7.5.2 Validation -- 7.5.3 Interpretation -- 7.6 Conclusions and Recommendations -- 7.7 Open Issues -- Part III Methods for Complex Multiblock Structures -- chapnumcolor8 Complex Block Structures -- with Focus on L-Shape Relations -- 8.i General Introduction -- 8.ii Relations to the General Framework -- 8.1 Analysis of L-shape Data: General Perspectives -- 8.2 Sequential Procedures for L-shape Data Based on PLS/PCR and ANOVA -- 8.2.1 Interpretation of X1, Quantitative X2-data, Horizontal Axis First -- 8.2.2 Interpretation of X1, Categorical X2-data, Horizontal Axis First -- 8.2.3 Analysis of Segments/Clusters of X1 Data -- 8.3 The L-PLS Method for Joint Estimation of Blocks in L-shape Data -- 8.3.1 The Original L-PLS Method, Endo-L-PLS -- 8.3.2 Exo- Versus Endo-L-PLS -- 8.4 Modifications of the Original L-PLS Idea -- 8.4.1 Weighting Information from X3 and X1 in L-PLS Using a Parameter "α -- 8.4.2 Three-blocks Bifocal PLS -- 8.5 Alternative L-shape Data Analysis Methods -- 8.5.1 Principal Component Analysis with External Information -- 8.5.2 A Simple PCA Based Procedure for Using Unlabelled Data in Calibration -- 8.5.3 Multivariate Curve Resolution for Incomplete Data -- 8.5.4 An Alternative Approach in Consumer Science Based on Correlations Between X3 and X1.; 8.6 Domino PLS and More Complex Data Structures -- 8.7 Conclusions and Recommendations -- 8.8 Open Issues -- Part IV Alternative Methods for Unsupervised and Supervised Topologies -- chapnumcolor9 Alternative Unsupervised Methods -- 9.i General Introduction -- 9.ii Relationship to the General Framework -- 9.1 Shared Variable Mode -- 9.2 Shared Sample Mode -- 9.2.1 Only Common Variation -- 9.2.1.1 DIABLO -- 9.2.1.2 Generalised Coupled Tensor Factorisation -- 9.2.1.3 Representation Matrices -- 9.2.1.4 Extended PCA -- 9.2.2 Common, Local, and Distinct Variation -- 9.2.2.1 Generalised SVD -- 9.2.2.2 Structural Learning and Integrative Decomposition -- 9.2.2.3 Bayesian Inter-battery Factor Analysis -- 9.2.2.4 Group Factor Analysis -- 9.2.2.5 OnPLS -- 9.2.2.6 Generalised Association Study -- 9.2.2.7 Multi-Omics Factor Analysis -- 9.3 Two Shared Modes and Only Common Variation -- 9.3.1 Generalised Procrustes Analysis -- 9.3.2 Three-way Methods -- 9.4 Conclusions and Recommendations -- 9.4.1 Open Issues -- chapnumcolor10 Alternative Supervised Methods -- 10.i General Introduction -- 10.ii Relations to the General Framework -- 10.1 Model and Focus -- 10.2 Extension of PCovR -- 10.2.1 Sparse Multiblock Principal Covariates Regression, Sparse PCovR -- 10.2.2 Multiway Multiblock Covariates Regression -- 10.3 Multiblock Redundancy Analysis -- 10.3.1 Standard Multiblock Redundancy Analysis -- 10.3.2 Sparse Multiblock ; Redundancy Analysis -- 10.4 Miscellaneous Multiblock Regression Methods -- 10.4.1 Multiblock Variance Partitioning -- 10.4.2 Network Induced Supervised Learning -- 10.4.3 Common Dimensions for Multiblock Regression -- 10.5 Modifications and Extensions of the SO-PLS Method -- 10.5.1 Extensions of SO-PLS to Three-Way Data -- 10.5.2 Variable Selection for SO-PLS -- 10.5.3 More Complicated Error Structure for SO-PLS.; 10.5.4 SO-PLS Used for Path Modelling.

Hedgehog Signaling in Organogenesis and Tumor Microenvironment

• Record Nr.: UNINA9910557628803321
• Autore: Shimo Tsuyoshi
• Pubbl/distr/stampa: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (172 p.)
• Soggetti: Medicine
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: The Hedgehog signaling pathway is an evolutionarily conserved pathway that governs complex developmental processes, including stem cell maintenance, proliferation, differentiation, and patterning. Several recent studies have shown that the aberrant activation of Hedgehog signaling is associated with neoplastic transformation, cancer cell proliferation, metastasis, multiple cancers' drug resistance, and survival rates. This book focuses on several aspects of Hedgehog signaling in organogenesis and the tumor microenvironment, and presents reviews and original papers on recent efforts in the field of Hedgehog signaling.