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Bioinformatics for Biomedical Science and Clinical Applications

Book Description

Contemporary biomedical and clinical research is undergoing constant development thanks to the rapid advancement of various high throughput technologies at the DNA, RNA and protein levels. These technologies can generate vast amounts of raw data, making bioinformatics methodologies essential in their use for basic biomedical and clinical applications.

Bioinformatics for biomedical science and clinical applications demonstrates what these cutting-edge technologies can do and examines how to design an appropriate study, including how to deal with data and address specific clinical questions. The first two chapters consider Bioinformatics and analysis of the human genome. The subsequent three chapters cover the introduction of Transcriptomics, Proteomics and Systems biomedical science. The remaining chapters move on to critical developments, clinical information and conclude with domain knowledge and adaptivity.

  • A coherent presentation of concepts, methodologies and practical tools that systematically lead to significant discoveries in the biomedical and clinical area
  • Real examples of cutting edge discoveries
  • The introduction of study types and technologies for all the DNA, RNA and protein levels

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of figures and tables
  6. Preface
  7. About the author
  8. Chapter 1: Introduction
    1. Abstract:
    2. 1.1 Complex systems: From uncertainty to predictability
    3. 1.2 Harnessing omics technology
    4. 1.3 Bioinformatics: From theory to practice
    5. 1.4 Take home messages
  9. Chapter 2: Genomics
    1. Abstract:
    2. 2.1 Introduction
    3. 2.2 The human genome and variome
    4. 2.3 Genomic platforms and platform level analysis
    5. 2.4 Study designs and contrast level analysis of GWAS
    6. 2.5 Adaptive exploration of interactions of multiple genes
    7. 2.6 Somatic genomic alterations and cancer
    8. 2.7 Case studies
    9. 2.8 Take home messages
  10. Chapter 3: Transcriptomics
    1. Abstract:
    2. 3.1 Introduction
    3. 3.2 Transcriptomic platforms at a glance
    4. 3.3 Platform level analysis for transcriptomics
    5. 3.4 Contrast level analysis and global visualization
    6. 3.5 Module level analysis
    7. 3.6 Systems level analysis for causal inference
    8. 3.7 RNA secondary structure analysis
    9. 3.8 Case studies
    10. 3.9 Take home messages
  11. Chapter 4: Proteomics
    1. Abstract:
    2. 4.1 Introduction
    3. 4.2 Proteomics platforms at a glance
    4. 4.3 Protein identification by MS based proteomics
    5. 4.4 From protein sequences to structures
    6. 4.5 Protein interaction networks
    7. 4.6 Case studies
    8. 4.7 Take home messages
  12. Chapter 5: Systems biomedical science
    1. Abstract:
    2. 5.1 Introduction
    3. 5.2 Cell level technology and resources at a glance
    4. 5.3 Conceptual frameworks from top-down
    5. 5.4 Systems construction from bottom-up and top-down
    6. 5.5 Specific directions of systems biomedical science
    7. 5.6 Case studies
    8. 5.7 Take home messages
  13. Chapter 6: Clinical developments
    1. Abstract:
    2. 6.1 Fulfilling unmet medical needs
    3. 6.2 Translational medicine
    4. 6.3 Clinical product development
    5. 6.4 Critical use of clinical information
    6. 6.5 Case studies
    7. 6.6 Take home messages
  14. Chapter 7: Conclusions
    1. Abstract:
    2. 7.1 Change and move forward
    3. 7.2 Presentation, presentation, presentation
    4. 7.3 Domain knowledge plus adaptivity
  15. Index