Alternative perspectives on psychiatric validation : DSM, IDC, RDoC, and Beyond / / edited by Peter Zachar [and three others] ; contributors, Massimiliano Aragona [and twenty-three others]

• Record Nr.: UNINA9910788088303321
• Pubbl/distr/stampa: New York, New York : , : Oxford University Press, , 2015; ©2015
• ISBN: 0-19-150204-9; 0-19-176069-2; 0-19-150203-0
• Descrizione fisica: 1 online resource (287 p.)
• Collana: International Perspectives in Philosophy and Psychiatry
• Disciplina: 618.928914
• Soggetti: Child psychotherapy
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Cover; Alternative Perspectives on Psychiatric ValidationDSM, ICD, RDoC, and Beyond; Copyright; Contents; List of Figures and Tables; List of Contributors; Part 1 Prologue; 1 Introduction: The concept of validation in psychiatry and psychology; Part 2 Matters more philosophical; 2 Rethinking received views on the history of psychiatric nosology: Minor shifts, major continuities; 3 Reality and utility unbound: An argument for dual-track nosologic validation; 4 Validity, realism, and normativity; 5 Natural and para-natural kinds in psychiatry; 6 The background assumptions of measurement practices in psychological assessment and psychiatric diagnosis 7 Neuroimaging in psychiatry: Epistemological considerations; 8 Translational validity across neuroscience and psychiatry; 9 Psychiatry, objectivity, and realism about value; 10 Scientific validity in psychiatry: Necessarily a moving target? ; Part 3 Matters (slightly) more clinical; 11 The importance of structural validity; 12 Validation of psychiatric classifications: The psychobiological model of personality as an exemplar; 13 Person-centered integrative diagnosis: Bases, models, and guides14 The four domains of mental illness (FDMI): An alternative to the DSM-5; ; Part 4 Epilogue; 15 United in diversity: Are there convergent models of psychiatric validity?; Index
• Sommario/riassunto: Many of the current debates about validity in psychiatry and psychology are predicated on the unexpected failure to validate commonly used diagnostic categories. The recognition of this failure has resulted in, what Thomas Kuhn calls, a period of extraordinary science in which validation problems are given increased weight, alternatives are proposed, methodologies are debated, and philosophical and historical analyses are seen as more relevant than usual. In this important new book in the IPPP series, a group of leading thinkers in psychiatry, psychology, and philosophy offer alternative persp

Data mining for genomics and proteomics : analysis of gene and protein expression data / / Darius M. Dzuida

• Record Nr.: UNINA9910140840803321
• Autore: Dziuda Darius M
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley, c2010
• ISBN: 9786612707575; 9781282707573; 1282707574; 9780470593417; 0470593415; 9780470593400; 0470593407
• Descrizione fisica: 1 online resource (348 p.)
• Collana: Wiley Series on Methods and Applications in Data Mining ; ; v.1
• Disciplina: 572.8/6
• Soggetti: Genomics - Data processing; Proteomics - Data processing; Data mining
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: DATA MINING FOR GENOMICS AND PROTEOMICS; CONTENTS; PREFACE; ACKNOWLEDGMENTS; 1 INTRODUCTION; 1.1 Basic Terminology; 1.1.1 ; The Central Dogma of Molecular Biology; 1.1.2 Genome; 1.1.3 Proteome; 1.1.4 DNA (Deoxyribonucleic Acid); 1.1.5 RNA (Ribonucleic Acid); 1.1.6 mRNA (messenger RNA); 1.1.7 Genetic Code; 1.1.8 Gene; 1.1.9 Gene Expression and the Gene Expression Level; 1.1.10 Protein; 1.2 Overlapping Areas of Research; 1.2.1 Genomics; 1.2.2 Proteomics; 1.2.3 Bioinformatics; 1.2.4 Transcriptomics and Other -omics . . .; 1.2.5 Data Mining; 2 BASIC ANALYSIS OF GENE EXPRESSION MICROARRAY DATA; 2.1 Introduction2.2 Microarray Technology; 2.2.1 Spotted Microarrays; 2.2.2 Affymetrix GeneChip(®) Microarrays; 2.2.3 Bead-Based Microarrays; 2.3 Low-Level Preprocessing of Affymetrix Microarrays; 2.3.1 MAS5; 2.3.2 RMA; 2.3.3 GCRMA; 2.3.4 PLIER; 2.4 Public Repositories of Microarray Data; 2.4.1 Microarray Gene Expression Data Society (MGED) Standards; 2.4.2 Public Databases; 2.4.2.1 Gene Expression Omnibus (GEO); 2.4.2.2 ArrayExpress; 2.5 Gene Expression Matrix; 2.5.1 Elements of Gene Expression Microarray Data Analysis; 2.6 Additional Preprocessing, Quality Assessment, and Filtering; 2.6.1 Quality Assessment2.6.2 Filtering; 2.7 Basic Exploratory Data Analysis; 2.7.1 t Test; 2.7.1.1 t Test for Equal Variances; 2.7.1.2 t Test for Unequal Variances; 2.7.2 ANOVA F Test; 2.7.3 SAM t Statistic; 2.7.4 Limma; 2.7.5 Adjustment for Multiple Comparisons; 2.7.5.1 Single-Step Bonferroni Procedure; 2.7.5.2 Single-Step Sidak Procedure; 2.7.5.3 Step-Down Holm Procedure; 2.7.5.4 Step-Up Benjamini and Hochberg Procedure; 2.7.5.5 Permutation Based Multiplicity Adjustment; 2.8 Unsupervised Learning (Taxonomy-Related Analysis); 2.8.1 Cluster Analysis; 2.8.1.1 Measures of Similarity or Distance2.8.1.2 K-Means Clustering; 2.8.1.3 Hierarchical Clustering; 2.8.1.4 Two-Way Clustering and Related Methods; 2.8.2 Principal Component Analysis; 2.8.3 Self-Organizing Maps; Exercises; 3 BIOMARKER DISCOVERY AND CLASSIFICATION; 3.1 Overview; 3.1.1 Gene Expression Matrix . . . Again; 3.1.2 Biomarker Discovery; 3.1.3 Classification Systems; 3.1.3.1 Parametric and Nonparametric Learning Algorithms; 3.1.3.2 Terms Associated with Common Assumptions Underlying Parametric Learning Algorithms; 3.1.3.3 Visualization of Classification Results; 3.1.4 Validation of the Classification Model3.1.4.1 Reclassification; 3.1.4.2 Leave-One-Out and K-Fold Cross-Validation; 3.1.4.3 External and Internal Cross-Validation; 3.1.4.4 Holdout Method of Validation; 3.1.4.5 Ensemble-Based Validation (Using Out-of-Bag Samples); 3.1.4.6 Validation on an Independent Data Set; 3.1.5 Reporting Validation Results; 3.1.5.1 Binary Classifiers; 3.1.5.2 Multiclass Classifiers; 3.1.6 Identifying Biological Processes Underlying the Class Differentiation; 3.2 Feature Selection; 3.2.1 Introduction; 3.2.2 Univariate Versus Multivariate Approaches; 3.2.3 Supervised Versus Unsupervised Methods
• Sommario/riassunto: Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.