Integrating omics data /

Tutorial chapters by leaders in the field introduce state-of-the-art methods to handle information integration problems of omics data.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Tseng, George (Editor ), Ghosha, Debāśisa (Editor ), Zhou, Xianghong Jasmine (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Cambridge University Press, 2015.
Temas:
Genomics > Statistical methods.
Meta-analysis.
Genomics > methods
Génomique > Méthodes statistiques.
Méta-analyse.
SCIENCE > Life Sciences > Biochemistry.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half title
  • Title
  • Copyright
  • Contents
  • Contributors
  • Introduction
  • Part A: Horizontal Meta-Analysis
  • 1. Meta-Analysis of Genome-Wide Association Studies: A Practical Guide
  • 2. MetaOmics: Transcriptomic Meta-Analysis Methods for Biomarker Detection, Pathway Analysis and Other Exploratory Purposes
  • 3. Integrative Analysis of Many Biological Networks to Study Gene Regulation
  • 4. Network Integration of Genetically Regulated Gene Expression to Study Complex Diseases
  • 5. Integrative Analysis of Multiple ChIP-X Data Sets Using Correlation Motifs
  • Part B: Vertical Integrative Analysis (General Methods)
  • 6. Identify Multi-Dimensional Modules from Diverse Cancer Genomics Data
  • 7. A Latent Variable Approach for Integrative Clustering of Multiple Genomic Data Types
  • 8. Penalized Integrative Analysis of High-Dimensional Omics Data
  • 9. A Bayesian Graphical Model for Integrative Analysis of TCGA Data: BayesGraph for TCGA Integration
  • 10. Bayesian Models for Flexible Integrative Analysis of Multi-Platform Genomics Data
  • 11. Exploratory Methods to Integrate Multisource Data
  • Part C: Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
  • 12. eQTL and Directed Graphical Model
  • 13. MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
  • 14. Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
  • 15. Analyzing Combinations of Somatic Mutations in Cancer Genomes
  • 16. A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
  • 17. From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
  • 18. Data Integration on Noncoding RNA Studies
  • 19. Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile.

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